{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8def206e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "import cantera as ct\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import math as math\n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "45f9e79f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "*********** Solving on 100 point grid with energy equation enabled ***********\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     6.328e-06      6.549\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     2.136e-05      6.406\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     3.604e-05      6.404\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     5.406e-05      6.006\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     0.0002737      4.982\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     0.0001299      5.028\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps       0.00111      4.299\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [100] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "grid refinement disabled.\n",
      "\n",
      "******************** Solving with grid refinement enabled ********************\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [100] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "##############################################################################\n",
      "Refining grid in flame.\n",
      "    New points inserted after grid points 33 34 35 36 37 \n",
      "    to resolve AXC5H10 C2H C2H2 C2H3 C2H3CHO C2H3CO C2H4 C2H5 C2H5O2 C2H6 C3H2 C3H3 C3H4XA C3H4XP C3H5O C3H5XA C3H5XT C3H6 C3H8 C4H10 C4H6 C4H7 C4H8X1 C5H10X1 C5H9 C6H11 C6H12X1 CH CH2 CH2CHO CH2CO CH2GSG CH2O CH2OH CH3 CH3CHO CH3COCH2 CH3COCH3 CH3O CH3O2 CH3O2H CH3OH CH4 CO CXC8H17 H2O H2O2 HCCO HCN HCO HNCO IC4H8O2HXT IXC3H5CHO IXC3H5CO IXC3H6CO IXC3H7 IXC3H7O2 IXC4H10 IXC4H6OH IXC4H7 IXC4H7O IXC4H7OH IXC4H8 IXC4H9 IXC4H9O2 IXC5H9 NEOXC5H11 NO2 NXC3H7 NXC3H7O2 O OH PXC4H9O2 TXC4H9 TXC4H9O2 XXC7H13 YXC7H14 YXC7H15 \n",
      "##############################################################################\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     0.0002563      4.161\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [105] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "##############################################################################\n",
      "Refining grid in flame.\n",
      "    New points inserted after grid points 34 35 36 37 38 39 \n",
      "    to resolve AXC5H10 C2H C2H2 C2H3 C2H4 C2H5 C2H5O2 C2H6 C3H2 C3H3 C3H4XA C3H4XP C3H5O C3H5XA C3H5XT C3H8 C4H10 C4H6 C4H7 C4H8X1 C5H10X1 C5H9 CH CH2CCH2OH CH2CHO CH2GSG CH3 CH3CHO CH3COCH2 CH3COCH3 CH3O CH3O2 HCCO HCNO HCO HO2 IXC3H5CO IXC3H6CO IXC3H7 IXC3H7O2 IXC4H10 IXC4H7 IXC4H8 IXC4H9 IXC4H9O2 IXC5H9 NEOXC5H11 NXC3H7 TXC4H9 TXC4H9O2 XXC7H13 YXC7H14 \n",
      "##############################################################################\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     0.0003844      4.491\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [111] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "##############################################################################\n",
      "Refining grid in flame.\n",
      "    New points inserted after grid points 36 37 38 39 40 41 42 43 \n",
      "    to resolve C2H C3H5XT C4H7 CH2CCH2OH CH2CHO CH3O IC4H8O2HXT IXC3H7 IXC3H7O2 IXC4H9 IXC4H9O2 NXC3H7 NXC3H7O2 TXC4H9O2 \n",
      "##############################################################################\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     0.0003844      4.475\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [119] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "no new points needed in flame\n"
     ]
    }
   ],
   "source": [
    "# Set up the Cantera flame configuration\n",
    "#gas = ct.Solution('gri30.yaml')  # Replace 'your_mechanism.cti' with the actual mechanism file\n",
    "gas = ct.Solution('Jerzembeck.yaml')\n",
    "phi = 0.75\n",
    "P = 1.0 * ct.one_atm\n",
    "Tin = 300.0\n",
    "width = 0.02  # Adjust the domain width as needed\n",
    "initial_grid = np.linspace(0, width, 100)\n",
    "\n",
    "# Create a stoichiometric CH4/Air premixed mixture\n",
    "#gas.set_equivalence_ratio(phi, \"CH4\", {\"O2\": 1.0, \"N2\": 3.76})\n",
    "gas.set_equivalence_ratio(phi, 'IXC8H18', 'O2:12.5, N2:47')\n",
    "gas.TP = Tin, P\n",
    "flame = ct.FreeFlame(gas, grid=initial_grid)\n",
    "\n",
    "\n",
    "# Run the flame simulation\n",
    "flame.solve(loglevel=1, auto=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ad54b698",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "48\n"
     ]
    }
   ],
   "source": [
    "time_scale = np.zeros(len(flame.grid))\n",
    "#time_scale = []\n",
    "# Calculate the time scale\n",
    "def calculate_time_scale(grid, velocity):\n",
    "     \n",
    "    for j in range(1,len(flame.grid)-1):\n",
    "        delta_x = (grid[j+1]-grid[j-1])/2\n",
    "        vel = velocity[j]\n",
    "        time_scale[j] = delta_x/vel\n",
    "        #time_scale.append(delta_x/vel)\n",
    "    return time_scale\n",
    "\n",
    "velocity = flame.velocity\n",
    "grid = flame.grid\n",
    "time_scale = calculate_time_scale(flame.grid, velocity)\n",
    "time_scale[0] = (grid[0]-grid[1])/velocity[0]\n",
    "time_scale[-1] = (grid[-2]-grid[-1])/velocity[-1]\n",
    "#length_flame = len(flame.grid)\n",
    "#print(np.diff(flame.grid))\n",
    "#print(len(time_scale))\n",
    "\n",
    "new_grid = np.zeros(len(flame.grid))\n",
    "i = 1\n",
    "for i in range(1,len(flame.grid)):\n",
    "    new_grid[i] = flame.grid[i]- flame.grid[i-1]\n",
    "    time_scale[i] = new_grid[i]/velocity[i]\n",
    "# Find the position of maximum heat release\n",
    "max_heat_release_idx = np.argmax(flame.heat_release_rate)\n",
    "max_heat_release = flame.heat_release_rate\n",
    "print(max_heat_release_idx)\n",
    "# to store the changed time scale t = 0 at maximum heat release\n",
    "actual_time_scale = np.zeros(len(time_scale))\n",
    "i = 1\n",
    "for i in range(41,len(time_scale)):\n",
    "    actual_time_scale[40] =  0\n",
    "    actual_time_scale[i] =  time_scale[i] + actual_time_scale[i-1]\n",
    "for i in range(39,-1,-1):\n",
    "    actual_time_scale[40] =  0\n",
    "    actual_time_scale[i] =  actual_time_scale[i+1] - time_scale[i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "49304c9f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 600.          600.          600.          600.          600.\n",
      "  600.          600.          600.          600.          600.\n",
      "  600.          600.          600.          599.99999999  599.99999999\n",
      "  599.99999999  599.99999999  599.99999999  599.99999999  599.99999999\n",
      "  599.99999999  599.99999999  599.99999999  599.99999999  599.99999999\n",
      "  599.99999999  599.99999999  600.00000008  600.00000142  600.00002359\n",
      "  600.00039036  600.00645645  600.10676984  601.76284657  612.12731754\n",
      "  646.1429701   707.16156187  773.37278593  823.98299646  890.55681034\n",
      "  975.81656101 1081.74598278 1209.04455385 1280.48043978 1356.38822004\n",
      " 1435.09965787 1513.63423421 1587.75737539 1653.22883548 1707.51438548\n",
      " 1750.69997318 1810.4377899  1851.21747197 1880.9661911  1918.79738634\n",
      " 1952.88738208 1973.03945191 1980.28445672 1984.19204268 1985.43091073\n",
      " 1985.92366841 1986.02082318 1986.03891098 1986.04112391 1986.04014338\n",
      " 1986.0385121  1986.03674089 1986.0349337  1986.03311284 1986.031284\n",
      " 1986.02944933 1986.02761009 1986.02576718 1986.02392134 1986.02207316\n",
      " 1986.02022309 1986.01837154 1986.01651881 1986.01466516 1986.0128108\n",
      " 1986.01095589 1986.00910057 1986.00724495 1986.00538912 1986.00353315\n",
      " 1986.0016771  1985.99982103 1985.99796497 1985.99610895 1985.994253\n",
      " 1985.99239713 1985.99054138 1985.98868574 1985.98683024 1985.98497488\n",
      " 1985.98311966 1985.98126461 1985.97940971 1985.97755497 1985.9757004\n",
      " 1985.97384601 1985.97199178 1985.97013773 1985.96828385 1985.96643015\n",
      " 1985.96457663 1985.96272328 1985.96087012 1985.95901713 1985.95716433\n",
      " 1985.95531171 1985.95345926 1985.951607   1985.94975492 1985.94790302\n",
      " 1985.94605131 1985.94419979 1985.94234851 1985.94049792 1985.93865127\n",
      " 1985.93683308 1985.9352279  1985.9352279 ]\n"
     ]
    }
   ],
   "source": [
    "print(flame.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d4b730ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0. <Species N2>\n",
      "1. <Species AR>\n",
      "2. <Species O>\n",
      "3. <Species O2>\n",
      "4. <Species H2>\n",
      "5. <Species H>\n",
      "6. <Species OH>\n",
      "7. <Species H2O2>\n",
      "8. <Species H2O>\n",
      "9. <Species HO2>\n",
      "10. <Species HCO>\n",
      "11. <Species CH2>\n",
      "12. <Species CO>\n",
      "13. <Species CH2O>\n",
      "14. <Species CO2>\n",
      "15. <Species CH3>\n",
      "16. <Species CH4>\n",
      "17. <Species C2H4>\n",
      "18. <Species CH3OH>\n",
      "19. <Species C2H5>\n",
      "20. <Species C2H6>\n",
      "21. <Species CH3O>\n",
      "22. <Species HOCHO>\n",
      "23. <Species CH3O2>\n",
      "24. <Species CH3O2H>\n",
      "25. <Species C2H2>\n",
      "26. <Species CH2CO>\n",
      "27. <Species C3H4XA>\n",
      "28. <Species C3H4XP>\n",
      "29. <Species C3H5XA>\n",
      "30. <Species C2H3>\n",
      "31. <Species CH3CHO>\n",
      "32. <Species C2H5O2>\n",
      "33. <Species C3H3>\n",
      "34. <Species C3H6>\n",
      "35. <Species C2H3CHO>\n",
      "36. <Species CH3COCH2>\n",
      "37. <Species NXC3H7>\n",
      "38. <Species C3H8>\n",
      "39. <Species CH3COCH3>\n",
      "40. <Species IXC3H7O2>\n",
      "41. <Species NXC3H7O2>\n",
      "42. <Species C4H6>\n",
      "43. <Species C4H8X1>\n",
      "44. <Species IXC4H7>\n",
      "45. <Species IXC4H8>\n",
      "46. <Species IXC3H5CHO>\n",
      "47. <Species IXC3H6CO>\n",
      "48. <Species IXC4H10>\n",
      "49. <Species C4H10>\n",
      "50. <Species IXC3H5CO>\n",
      "51. <Species IXC4H7OH>\n",
      "52. <Species IXC4H9O2>\n",
      "53. <Species TXC4H9O2>\n",
      "54. <Species PXC4H9O2>\n",
      "55. <Species C5H9>\n",
      "56. <Species IXC5H9>\n",
      "57. <Species AXC5H10>\n",
      "58. <Species C5H10X1>\n",
      "59. <Species C6H11>\n",
      "60. <Species C6H12X1>\n",
      "61. <Species YXC7H14>\n",
      "62. <Species NXC7H16>\n",
      "63. <Species IXC8H18>\n",
      "64. <Species CH>\n",
      "65. <Species CH2GSG>\n",
      "66. <Species CH2OH>\n",
      "67. <Species HOCH2O>\n",
      "68. <Species C2H>\n",
      "69. <Species HCCO>\n",
      "70. <Species CH2CHO>\n",
      "71. <Species C2H5O>\n",
      "72. <Species C2H3O1X2>\n",
      "73. <Species CH3CO>\n",
      "74. <Species C3H2>\n",
      "75. <Species C3H5O>\n",
      "76. <Species C3H5XT>\n",
      "77. <Species IXC3H7>\n",
      "78. <Species C2H3CO>\n",
      "79. <Species CH2CCH2OH>\n",
      "80. <Species C4H7>\n",
      "81. <Species C4H7O>\n",
      "82. <Species IXC4H7O>\n",
      "83. <Species IXC4H9>\n",
      "84. <Species PXC4H9>\n",
      "85. <Species TXC4H9>\n",
      "86. <Species TXC4H9O>\n",
      "87. <Species IXC4H6OH>\n",
      "88. <Species C4H8OOH1X3>\n",
      "89. <Species IC4H8O2HXT>\n",
      "90. <Species TC4H8O2HXI>\n",
      "91. <Species NEOXC5H11>\n",
      "92. <Species C5H11X1>\n",
      "93. <Species C6H13X1>\n",
      "94. <Species XXC7H13>\n",
      "95. <Species XC7H13OXZ>\n",
      "96. <Species C7H15X2>\n",
      "97. <Species YXC7H15>\n",
      "98. <Species CXC8H17>\n",
      "99. <Species N>\n",
      "100. <Species NH>\n",
      "101. <Species NH2>\n",
      "102. <Species NH3>\n",
      "103. <Species NNH>\n",
      "104. <Species NO>\n",
      "105. <Species NO2>\n",
      "106. <Species N2O>\n",
      "107. <Species HNO>\n",
      "108. <Species CN>\n",
      "109. <Species HCN>\n",
      "110. <Species H2CN>\n",
      "111. <Species HCNN>\n",
      "112. <Species HCNO>\n",
      "113. <Species HOCN>\n",
      "114. <Species HNCO>\n",
      "115. <Species NCO>\n",
      "116. <Species C>\n"
     ]
    }
   ],
   "source": [
    "for i, specie in enumerate(gas.species()):\n",
    "    print(f\"{i}. {specie}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6a4374a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Extract concentration data\n",
    "X_C8H18 = flame.X[63]\n",
    "X_O2 = flame.X[3]\n",
    "X_CO = flame.X[12]\n",
    "X_NO = flame.X[104]\n",
    "X_CO2 = flame.X[14]\n",
    "X_H2O = flame.X[8]\n",
    "\n",
    "'''\n",
    "plt.figure()\n",
    "\n",
    "plt.plot(flame.grid * 100, X_C8H18, label=\"$C_{8}H_{18}$\")\n",
    "plt.plot(flame.grid * 100, X_CO2, label=\"$CO_{2}$\")\n",
    "plt.plot(flame.grid * 100, X_H2O, label=\"$H_{2}O$\")\n",
    "plt.plot(flame.grid * 100, X_O2, label=\"$O_{2}$\")\n",
    "plt.plot(flame.grid * 100, X_CO, label=\"$CO$\")\n",
    "plt.plot(flame.grid * 100, X_NO, label=\"$NO$\")\n",
    "\n",
    "plt.legend(loc=2, bbox_to_anchor=(1, 1))\n",
    "plt.xlabel(\"Distance (cm)\")\n",
    "plt.ylabel(\"MoleFractions\");\n",
    "'''\n",
    "fig, ax1 = plt.subplots()\n",
    "\n",
    "ax1.set_xlabel('Grid Points (cm)')\n",
    "ax1.set_ylabel('Temperature (K)', color='tab:blue')\n",
    "ax1.plot(flame.grid * 100, flame.T,label=\"Temperature\")\n",
    "\n",
    "ax1.tick_params(axis='y', labelcolor='tab:blue')\n",
    "\n",
    "ax2 = ax1.twinx()\n",
    "ax2.set_ylabel('Mass Fractions', color='tab:red')\n",
    "plt.plot(flame.grid * 100, X_C8H18, label=\"$C_{8}H_{18}$\",linestyle = '--')\n",
    "plt.plot(flame.grid * 100, X_CO2, label=\"$CO_{2}$\",linestyle = '--')\n",
    "plt.plot(flame.grid * 100, X_H2O, label=\"$H_{2}O$\",linestyle = '--')\n",
    "plt.plot(flame.grid * 100, X_O2, label=\"$O_{2}$\",linestyle = '--')\n",
    "plt.plot(flame.grid * 100, X_CO, label=\"$CO$\",linestyle = '--')\n",
    "plt.plot(flame.grid * 100, X_NO, label=\"$NO$\",linestyle = '--')\n",
    "ax2.tick_params(axis='y', labelcolor='tab:red')\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "# Add legends\n",
    "lines1, labels = ax1.get_legend_handles_labels()\n",
    "lines2, labels2 = ax2.get_legend_handles_labels()\n",
    "ax2.legend(lines1 + lines2, labels + labels2, loc='upper right', bbox_to_anchor=(1.45, 1))\n",
    "\n",
    "\n",
    "plt.title('Flame properties vs. Grid points (cm)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "c0df46a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 0.8)"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rq/H7ro/aoqKicOPGDSxbtgy//fYboqKi1F7Pycmp8p7yqwnLv0fVCQoKgoWFBebMmYOGDRuiXbt2AB6EqEOHDmHv3r0PnVUKDAxEo0aNsHjxYpSUlEjt8fHxVb4n+vozqklwcHCVHfQbNWqELl26YMWKFUhLS1N77WGzVbVx7NgxtGvXTue1WvTk4sxSHaSlpWHlypVIS0uT/gU5ceJEJCQkYOXKlZg5c6aRK6S68vLywowZMxAbG4urV69iwIABsLOzQ2pqKn788Ue88sormDhxItq0aQMvLy9MnDgR169fh729PTZs2KDxdENAQAAA4M0330RkZCSUSmWtbvswadIkbNmyBc8++yyGDx+OgIAAFBYW4uTJk/jhhx9w9erVak8zzJ49G3v27EFQUBBGjx6Ntm3bIicnB8ePH8fPP/8s/WJ/9tlnsXHjRgwcOBB9+/ZFamoqFi9ejLZt26KgoEA63qhRo5CTk4Nu3bqhSZMmuHbtGr744gv4+fnh6aefrnO92vTs2VOa1RozZgwKCgqwdOlSODs7Iz09Xa1vQEAAFi1ahBkzZsDb2xvOzs7o1q2bXmrr06cP7OzsMHHiRCmQVvTRRx9h37596Nu3Lzw9PXHz5k189dVXaNKkCTp37qz12PXq1UNAQAAOHTok7bEEPJhZKiwsRGFh4UPDkoWFBWbMmIExY8agW7duiIqKQmpqKlauXFllzZKXlxccHR2xePFi2NnZwdbWFkFBQdWuGdNF//798c033+DChQtqMzv//e9/0blzZ/zjH//AK6+8gubNm+Pq1av46aef9LqT+P3797F37168/vrrejsmPQEMe/GdaQMgfvzxR+n5tm3bBABha2ur9jA3NxeDBg2q8v7o6GjRv39/rZ8RFhYm3nrrLf0WTpLyrQN+/fVXja+HhYWpbR1QbsOGDaJz587Sz7hNmzZi7Nix4vz581KfM2fOiIiICFG/fn3h5OQkRo8eLX777bcql2CXlpaKN954QzRq1EgoFIqHbiPg6ekp+vbtq/G1O3fuiNjYWOHt7S0sLS2Fk5OTCAkJEfPmzRMlJSVSP1TaOkAIITIzM8XYsWOFh4eHsLCwEK6urqJ79+5iyZIlUh+VSiVmzpwpPD09hZWVlfD39xfbtm0T0dHRwtPTU+r3ww8/iJ49ewpnZ2dhaWkpmjZtKsaMGSPS09NrVa+u34ctW7YIHx8fYW1tLZo1aybmzJkjVqxYIQCI1NRUqV9GRobo27evsLOzq3K5fF1qKzd06FABQERERFR5LTExUfTv31+4u7sLS0tL4e7uLoYMGVJlu4LqTJo0SQAQc+bMUWv39vYWAMTly5fV2itvHVDuq6++Es2bNxdWVlYiMDBQ7Nu3T4SFhVXZUmHz5s2ibdu2wtzcXO3PcHV/Ryr/mahOcXGxcHJyEtOnT6/y2qlTp8TAgQOFo6OjsLa2Fq1btxZTpkyRXi/fOqDy9gvR0dHC1ta2yvE01bpjxw4BQFy8ePGhtRKVUwghwxznY0qhUKhdDbd27VoMHToUp0+frrK4sH79+nB1dVVrGz58OHJzc6tcDVdReHg4/Pz8eDUcET22pk+fjpUrV+LixYsGvz/cgAEDpP+XE9UU1yzVgb+/P8rKynDz5k14e3urPSoHJSIiemDChAkoKCjAmjVrDPq5Z8+exbZt2zB9+nSDfi6ZPq5ZeoiCggJcunRJep6amoqUlBQ0bNgQrVq1wtChQzFs2DB8+umn8Pf3R1ZWFhITE+Hj44O+ffsCeHAPpZKSEuTk5ODOnTvS+ffyxZ0ApLaCggJkZWUhJSUFlpaWaNu2raGGSkRkEPXr16/Rvlr69vTTT9f6QgJ6svE03EMkJSWha9euVdqjo6MRHx+P+/fvY8aMGVi9ejWuX78OJycndOrUCdOmTUOHDh0APNgaoHxDuIoqfus1Xarr6emJq1ev6m8wREREpDOGJSIiIiItuGaJiIiISAuGJSIiIiItuMBbA5VKhRs3bsDOzo43WiQiIjIRQgjcuXMH7u7uWm9QrSuGJQ1u3LgBDw8PY5dBREREtfDHH3+gSZMmejsew5IGdnZ2AB58s+3t7Y1cDREREdVEfn4+PDw8pN/j+sKwpEH5qTd7e3uGJSIiIhOj7yU0XOBNREREpAXDEhEREZEWDEtEREREWjAsEREREWnBsERERESkBcMSERERkRYMS0RERERaMCwRERERacGwRERERKQFwxIRERGRFgxLRERERFowLBERERFpwbBERERkooQQuHe/zNhlPPYYloiIiEzUW2tS0GZKAq5mFxq7lMcawxIREZGJ2vLbDQDAquSrxi3kMcewREREZOJKy4SxS3isMSwRERGZuFKVytglPNYYloiIiEwcZ5bkxbBERERk4kpVDEtyMmpY2rdvH/r16wd3d3coFAps2rRJa//hw4dDoVBUebRr107q8+GHH1Z5vU2bNjKPhIiIyHjul/E0nJyMGpYKCwvh6+uLhQsX1qj/ggULkJ6eLj3++OMPNGzYEC+88IJav3bt2qn1O3DggBzlExERPRJ4Gk5e5sb88N69e6N379417u/g4AAHBwfp+aZNm3D79m2MGDFCrZ+5uTlcXV31VicREdGjjAu85WXSa5aWL1+OiIgIeHp6qrVfvHgR7u7uaNGiBYYOHYq0tDStxykuLkZ+fr7ag4iIyFRwzZK8TDYs3bhxAzt27MCoUaPU2oOCghAfH4+EhAQsWrQIqampCA0NxZ07d6o91qxZs6RZKwcHB3h4eMhdPhEREZkIkw1Lq1atgqOjIwYMGKDW3rt3b7zwwgvw8fFBZGQktm/fjtzcXKxbt67aY8XGxiIvL096/PHHHzJXT0RERKbCqGuWaksIgRUrVuCll16CpaWl1r6Ojo5o1aoVLl26VG0fKysrWFlZ6btMIiIiegyY5MzS3r17cenSJYwcOfKhfQsKCnD58mW4ubkZoDIiIiJ63Bg1LBUUFCAlJQUpKSkAgNTUVKSkpEgLsmNjYzFs2LAq71u+fDmCgoLQvn37Kq9NnDgRe/fuxdWrV/HLL79g4MCBUCqVGDJkiKxjISIioseTUU/DHT16FF27dpWex8TEAACio6MRHx+P9PT0Kley5eXlYcOGDViwYIHGY/75558YMmQIbt26hUaNGqFz5844dOgQGjVqJN9AiIiI6LFl1LAUHh4OIaq/3DE+Pr5Km4ODA4qKiqp9z5o1a/RRGhERkclQGLuAx5xJrlkiIiIiMhSGJSIiIiItGJaIiIiItGBYIiIiItKCYYmIiMjE8c5w8mJYIiIiItKCYYmIiIhIC4YlIiIiIi0YloiIiIi0YFgiIiIi0oJhiYiIyMTxdifyYlgiIiIi0oJhiYiIiEgLhiUiIiIiLRiWiIiIiLRgWCIiIiLSgmGJiIiISAuGJSIiIiItGJaIiIiItGBYIiIiItKCYYmIiIhIC4YlIiIiE6dQ8IYncmJYIiIiItKCYYmIiIhIC4YlIiIiIi0YloiIiIi0YFgiIiIi0oJhiYiIiEgLhiUiIiIiLRiWiIiIiLRgWCIiIiLSgmGJiIiISAuGJSIiIiItGJaIiIhMkBBC+pp3hpMXwxIREZEJqpCVIKrvRnrAsERERGSCKgYkzizJy6hhad++fejXrx/c3d2hUCiwadMmrf2TkpKgUCiqPDIyMtT6LVy4EM2aNYO1tTWCgoJw5MgRGUdBRERkXJxZkpdRw1JhYSF8fX2xcOFCnd53/vx5pKenSw9nZ2fptbVr1yImJgZTp07F8ePH4evri8jISNy8eVPf5RMRERlNxTVLJC9zY35479690bt3b53f5+zsDEdHR42vffbZZxg9ejRGjBgBAFi8eDF++uknrFixAu+9915dyiUiInokMTjJyyTXLPn5+cHNzQ09evTAwYMHpfaSkhIcO3YMERERUpuZmRkiIiKQnJxc7fGKi4uRn5+v9iAiInqUMR4ZjkmFJTc3NyxevBgbNmzAhg0b4OHhgfDwcBw/fhwAkJ2djbKyMri4uKi9z8XFpcq6popmzZoFBwcH6eHh4SHrOIiIiOqKk0mGY9TTcLpq3bo1WrduLT0PCQnB5cuX8fnnn+Obb76p9XFjY2MRExMjPc/Pz2dgIiIik8HcJC+TCkuadOzYEQcOHAAAODk5QalUIjMzU61PZmYmXF1dqz2GlZUVrKysZK2TiIhInwQjksGY1Gk4TVJSUuDm5gYAsLS0REBAABITE6XXVSoVEhMTERwcbKwSiYiIyIQZdWapoKAAly5dkp6npqYiJSUFDRs2RNOmTREbG4vr169j9erVAID58+ejefPmaNeuHe7du4dly5bhf//7H3bt2iUdIyYmBtHR0QgMDETHjh0xf/58FBYWSlfHERERPQ64ZslwjBqWjh49iq5du0rPy9cNRUdHIz4+Hunp6UhLS5NeLykpwdtvv43r16+jXr168PHxwc8//6x2jKioKGRlZSEuLg4ZGRnw8/NDQkJClUXfREREjwsGJ3kpBDdnqCI/Px8ODg7Iy8uDvb29scshIiKq4t79MrSZkgAACGvVCKte7mjkioxPrt/fJr9miYiI6EnEG+kaDsMSERERkRYMS0RERCaIWwcYDsMSERGRiePyY3kxLBEREZkg5iPDYVgiIiIyQcxKhsOwRERERKQFwxIREZEJ4jolw2FYIiIiMnHMTfJiWCIiIjJBzEeGw7BEREREpAXDEhERkQniqTfDYVgiIiIyRQxLBsOwREREZOJ46xN5MSwRERGZIAYkw2FYIiIiMnFcvyQvhiUiIiITxIBkOAxLREREJo7BSV4MS0RERCaI+chwGJaIiIhMEO8NZzgMS0RERCaOV8bJi2GJiIjIBDEeGQ7DEhEREZEWDEtEREQmqOKSJS5fkhfDEhERkQmquE6JWUleDEtERESmjmlJVgxLREREpqjiaTimJVkxLBEREZk4rlmSF8MSERGRCRLVfE36x7BERERk4ribt7wYloiIiEyQ2tYBxivjiWCu6xtSU1Oxf/9+XLt2DUVFRWjUqBH8/f0RHBwMa2trOWokIiKiStS2DmBaklWNw9K3336LBQsW4OjRo3BxcYG7uztsbGyQk5ODy5cvw9raGkOHDsW7774LT09POWsmIiKiCpiV5FWjsOTv7w9LS0sMHz4cGzZsgIeHh9rrxcXFSE5Oxpo1axAYGIivvvoKL7zwgiwFExERUaXZJE4tyapGYWn27NmIjIys9nUrKyuEh4cjPDwcH3/8Ma5evaqv+oiIiOghGJXkVaMF3tqCUmVPPfUUAgICatR337596NevH9zd3aFQKLBp0yat/Tdu3IgePXqgUaNGsLe3R3BwMHbu3KnW58MPP4RCoVB7tGnTpsb1ExERmQIGJMPR+Wq448eP4+TJk9LzzZs3Y8CAAXj//fdRUlKi07EKCwvh6+uLhQsX1qj/vn370KNHD2zfvh3Hjh1D165d0a9fP5w4cUKtX7t27ZCeni49Dhw4oFNdREREj7qK2wXwLJy8dL4absyYMXjvvffQoUMHXLlyBYMHD8bAgQOxfv16FBUVYf78+TU+Vu/evdG7d+8a96987JkzZ2Lz5s3YunUr/P39pXZzc3O4urrW+LhERESmjLc7kZfOM0sXLlyAn58fAGD9+vXo0qULvvvuO8THx2PDhg36rk8rlUqFO3fuoGHDhmrtFy9ehLu7O1q0aIGhQ4ciLS3NoHURERHJTW2fJWYlWek8sySEgEqlAgD8/PPPePbZZwEAHh4eyM7O1m91DzFv3jwUFBRg0KBBUltQUBDi4+PRunVrpKenY9q0aQgNDcWpU6dgZ2en8TjFxcUoLi6Wnufn58teOxERkb4wLMlL57AUGBiIGTNmICIiAnv37sWiRYsAPNis0sXFRe8FVue7777DtGnTsHnzZjg7O0vtFU/r+fj4ICgoCJ6enli3bh1Gjhyp8VizZs3CtGnTZK+ZiIhIDsxK8tL5NNz8+fNx/PhxjBs3Dh988AG8vb0BAD/88ANCQkL0XqAma9aswahRo7Bu3TpERERo7evo6IhWrVrh0qVL1faJjY1FXl6e9Pjjjz/0XTIREZFseG84edV4ZunKlSto0aIFfHx81K6GK/fJJ59AqVTqtThNvv/+e7z88stYs2YN+vbt+9D+BQUFuHz5Ml566aVq+1hZWcHKykqfZRIREcmK+chwajyz5OPjg/bt2+P999/HkSNHqrxubW0NCwsLnT68oKAAKSkpSElJAfDgVF5KSoq0IDs2NhbDhg2T+n/33XcYNmwYPv30UwQFBSEjIwMZGRnIy8uT+kycOBF79+7F1atX8csvv2DgwIFQKpUYMmSITrURERE9ynhvOMOpcVjKzs7GrFmzcPPmTTz33HNwc3PD6NGjsXXrVty7d69WH3706FH4+/tLl/3HxMTA398fcXFxAID09HS1K9mWLFmC0tJSjB07Fm5ubtLjrbfekvr8+eefGDJkCFq3bo1BgwbhqaeewqFDh9CoUaNa1UhERPSo49YB8lKIWpzoFEIgOTkZW7ZswZYtW5CWloaIiAg899xz6Nevn8kHk/z8fDg4OCAvLw/29vbGLoeIiKiKq9mFCJ+XBABo6Vwfu2PCjFvQI0Cu3986L/AGAIVCgZCQEMyePRtnzpzBiRMnEBoaivj4eDRp0qTGO3ITERFR3XFeSV46bx2gScuWLfH222/j7bffxq1bt5CTk6OPwxIREVE1KgYkXg0nrxqHpS1btjz8YObmcHNzQ7t27epUFBEREWmndm84I9bxJKhxWBowYECND+rq6oq1a9ciNDS0NjURERERPTJqvGZJpVI99FFWVoYbN27g+eefV7tCjYiIiPRLVPuE9E0va5bKKRQKuLq6YtKkSWjdurU+D01ERETVYFaSV41mlg4dOlTjAxYVFaGgoACZmZm1LoqIiIi0q7immwu85VWjsPTSSy8hMjIS69evR2FhocY+Z86cwfvvvw8vLy8cO3YMDg4Oei2UiIiINGNUkleNTsOdOXMGixYtwuTJk/Gf//wHrVq1gru7O6ytrXH79m2cO3cOBQUFGDhwIHbt2oUOHTrIXTcREdETjrc7MZQahSULCwu8+eabePPNN3H06FEcOHAA165dw927d+Hr64sJEyaga9euaNiwodz1EhERESqdhuPckqx0XuAdGBiIwMBAOWohIiKiWuDMkrxqdbsTIiIiMi71HbyNVsYTgWGJiIiISAuGJSIiIhPErQMMh2GJiIjIBFVc1M2oJK86haV79+7pqw4iIiKqJU4syUvnsKRSqTB9+nQ0btwY9evXx5UrVwAAU6ZMwfLly/VeIBEREVXFgGQ4OoelGTNmID4+HnPnzoWlpaXU3r59eyxbtkyvxREREdHDcZ8leekcllavXo0lS5Zg6NChUCqVUruvry/OnTun1+KIiIhIM/UF3sar40mgc1i6fv06vL29q7SrVCrcv39fL0URERFRzTEryUvnsNS2bVvs37+/SvsPP/wAf39/vRRFRERE2gneG85gdL7dSVxcHKKjo3H9+nWoVCps3LgR58+fx+rVq7Ft2zY5aiQiIqJK1AMS05KcdJ5Z6t+/P7Zu3Yqff/4Ztra2iIuLw9mzZ7F161b06NFDjhqJiIhIC84syUvnmSUACA0Nxe7du/VdCxEREdUCs5K8dJ5ZatGiBW7dulWlPTc3Fy1atNBLUURERFRzvN2JvHQOS1evXkVZWVmV9uLiYly/fl0vRREREZF2alsHGK+MJ0KNT8Nt2bJF+nrnzp1wcHCQnpeVlSExMRHNmjXTa3FERESkGa+GM5wah6UBAwYAABQKBaKjo9Ves7CwQLNmzfDpp5/qtTgiIiJ6OJ6Gk1eNw5JKpQIANG/eHL/++iucnJxkK4qIiIi042k4w9H5arjU1FQ56iAiIiJ6JNVq64DCwkLs3bsXaWlpKCkpUXvtzTff1EthREREVD3uSWk4OoelEydOoE+fPigqKkJhYSEaNmyI7Oxs1KtXD87OzgxLREREBlBxnRKzkrx03jpgwoQJ6NevH27fvg0bGxscOnQI165dQ0BAAObNmydHjURERKQFF3jLS+ewlJKSgrfffhtmZmZQKpUoLi6Gh4cH5s6di/fff1+OGomIiKgSUc3XpH86hyULCwuYmT14m7OzM9LS0gAADg4O+OOPP/RbHRERET0UJ5bkpfOaJX9/f/z6669o2bIlwsLCEBcXh+zsbHzzzTdo3769HDUSERFRJepbBzAtyUnnmaWZM2fCzc0NAPDxxx+jQYMGeO2115CVlYUlS5bodKx9+/ahX79+cHd3h0KhwKZNmx76nqSkJPzjH/+AlZUVvL29ER8fX6XPwoUL0axZM1hbWyMoKAhHjhzRqS4iIqJHH3fwNhSdwpIQAs7OzggODgbw4DRcQkIC8vPzcezYMfj6+ur04YWFhfD19cXChQtr1D81NRV9+/ZF165dkZKSgvHjx2PUqFHYuXOn1Gft2rWIiYnB1KlTcfz4cfj6+iIyMhI3b97UqTYiIiJTwawkL4XQYQm9SqWCtbU1Tp8+jZYtW+q3EIUCP/74o3RbFU3effdd/PTTTzh16pTUNnjwYOTm5iIhIQEAEBQUhH/+85/48ssvpZo9PDzwxhtv4L333qtRLfn5+XBwcEBeXh7s7e1rPygiIiKZHL2ag38vTgYAWCrNcOHj3kauyPjk+v2t08ySmZkZWrZsiVu3bumtAF0kJycjIiJCrS0yMhLJyQ/+sJSUlODYsWNqfczMzBARESH10aS4uBj5+flqDyIiIlPBNUvy0nnN0uzZszFp0iS12R1DycjIgIuLi1qbi4sL8vPzcffuXWRnZ6OsrExjn4yMjGqPO2vWLDg4OEgPDw8PWeonIiLSF7WtA5iVZKXz1XDDhg1DUVERfH19YWlpCRsbG7XXc3Jy9FacocTGxiImJkZ6np+fz8BERESPNN5I13B0Dkvz58+XoYyacXV1RWZmplpbZmYm7O3tYWNjA6VSCaVSqbGPq6trtce1srKClZWVLDUTERGRadM5LEVHR8tRR40EBwdj+/btam27d++Wrs6ztLREQEAAEhMTpYXiKpUKiYmJGDdunKHLJSIiko3aveF4Hk5WOq9Z0qeCggKkpKQgJSUFwIOtAVJSUqRdwWNjYzFs2DCp/6uvvoorV67gnXfewblz5/DVV19h3bp1mDBhgtQnJiYGS5cuxapVq3D27Fm89tprKCwsxIgRIww6NiIiIkNhVJKXzjNL+nT06FF07dpVel6+big6Ohrx8fFIT0+XghMANG/eHD/99BMmTJiABQsWoEmTJli2bBkiIyOlPlFRUcjKykJcXBwyMjLg5+eHhISEKou+iYiITBkXeBuOTvssPSm4zxIRET3qki/fwpClh6TnV2f3NWI1j4ZHYp8lIiIiejRx7kM+tQ5Lly5dws6dO3H37l0A/CEREREZUuWNKPlrWD46h6Vbt24hIiICrVq1Qp8+fZCeng4AGDlyJN5++229F0hEREQPx6wkH53D0oQJE2Bubo60tDTUq1dPao+KipLuz0ZEREQyq5SOeIZHPjpfDbdr1y7s3LkTTZo0UWtv2bIlrl27prfCiIiIqHqVoxGjknx0nlkqLCxUm1Eql5OTw12wiYiIjIQTS/LROSyFhoZi9erV0nOFQgGVSoW5c+eq7ZlERERE8qkcjiov+Cb90fk03Ny5c9G9e3ccPXoUJSUleOedd3D69Gnk5OTg4MGDctRIRERED8GZJfnoPLPUvn17XLhwAZ07d0b//v1RWFiI559/HidOnICXl5ccNRIREVElnEkynFrd7sTBwQEffPCBvmshIiKiGuJMkuHoPLOUkJCAAwcOSM8XLlwIPz8//Oc//8Ht27f1WhwRERHVDMOTfHQOS5MmTUJ+fj4A4OTJk4iJiUGfPn2Qmpoq3QiXiIiI5FV16wCmJbnofBouNTUVbdu2BQBs2LAB/fr1w8yZM3H8+HH06dNH7wUSERHRw3FmST46zyxZWlqiqKgIAPDzzz+jZ8+eAICGDRtKM05EREQkr8o7djMryUfnmaXOnTsjJiYGzzzzDI4cOYK1a9cCAC5cuFBlV28iIiKSR5XTcJxako3OM0tffvklzM3N8cMPP2DRokVo3LgxAGDHjh3o1auX3gskIiKih2NUko/OM0tNmzbFtm3bqrR//vnneimIiIiIaqDKjXSNU8aToFb7LJW7d+8eSkpK1Nrs7e3rVBARERHpjqfh5FOrG+mOGzcOzs7OsLW1RYMGDdQeREREJL/KWwWomJVko3NYeuedd/C///0PixYtgpWVFZYtW4Zp06bB3d1d7Qa7REREJJ8qN9LlzJJsdD4Nt3XrVqxevRrh4eEYMWIEQkND4e3tDU9PT3z77bcYOnSoHHUSERGRFpxZko/OM0s5OTlo0aIFgAfrk3JycgA82FJg3759+q2OiIiINOLMkuHoHJZatGiB1NRUAECbNm2wbt06AA9mnBwdHfVaHBEREdUMo5J8dA5LI0aMwG+//QYAeO+997Bw4UJYW1tjwoQJmDRpkt4LJCIioqoqhyMVZ5Zko/OapQkTJkhfR0RE4Ny5czh27Bi8vb3h4+Oj1+KIiIhIs8qn3bhmST513mfJ09MTnp6e+qqHiIiIaoFrluSj82m4srIyTJ8+HY0bN0b9+vVx5coVAMCUKVOwfPlyvRdIREREVVW9N5xRyngi6ByWPv74Y8THx2Pu3LmwtLSU2tu3b49ly5bptTgiIiKqGa5Zko/OYWn16tVYsmQJhg4dCqVSKbX7+vri3Llzei2OiIiINKu6dYBx6ngS6ByWrl+/Dm9v7yrtKpUK9+/f10tRRERE9DCVF3gzLclF57DUtm1b7N+/v0r7Dz/8AH9/f70URURERLrh1XDy0flquLi4OERHR+P69etQqVTYuHEjzp8/j9WrV2Pbtm1y1EhERESVVJ1IYlqSi84zS/3798fWrVvx888/w9bWFnFxcTh79iy2bt2KHj16yFEjERERPQRnluRTq32WQkNDsXv3bn3XQkRERDXEHbwNR+eZJSIiIjI+Xg1nODWaWWrQoAEUCkWNDpiTk1OngoiIiEh3nFmST43C0vz582UtYuHChfjkk0+QkZEBX19ffPHFF+jYsaPGvuHh4di7d2+V9j59+uCnn34CAAwfPhyrVq1Sez0yMhIJCQn6L56IiMgIRKUTccxK8qlRWIqOjpatgLVr1yImJgaLFy9GUFAQ5s+fj8jISJw/fx7Ozs5V+m/cuBElJSXS81u3bsHX1xcvvPCCWr9evXph5cqV0nMrKyvZxkBERGRsDEvyqdWapcuXL2Py5MkYMmQIbt68CQDYsWMHTp8+rfOxPvvsM4wePRojRoxA27ZtsXjxYtSrVw8rVqzQ2L9hw4ZwdXWVHrt370a9evWqhCUrKyu1fg0aNNB9oERERI+oyuGIp+Hko3NY2rt3Lzp06IDDhw9j48aNKCgoAAD89ttvmDp1qk7HKikpwbFjxxAREfF3QWZmiIiIQHJyco2OsXz5cgwePBi2trZq7UlJSXB2dkbr1q3x2muv4datW9Ueo7i4GPn5+WoPIiKiRxmvhjMcncPSe++9hxkzZmD37t1qN9Lt1q0bDh06pNOxsrOzUVZWBhcXF7V2FxcXZGRkPPT9R44cwalTpzBq1Ci19l69emH16tVITEzEnDlzsHfvXvTu3RtlZWUajzNr1iw4ODhIDw8PD53GQUREZGyMSvLReZ+lkydP4rvvvqvS7uzsjOzsbL0UVVPLly9Hhw4dqiwGHzx4sPR1hw4d4OPjAy8vLyQlJaF79+5VjhMbG4uYmBjpeX5+PgMTERE90oSovMCbcUkuOs8sOTo6Ij09vUr7iRMn0LhxY52O5eTkBKVSiczMTLX2zMxMuLq6an1vYWEh1qxZg5EjRz70c1q0aAEnJydcunRJ4+tWVlawt7dXexAREZkS7uAtH53D0uDBg/Huu+8iIyMDCoUCKpUKBw8exMSJEzFs2DCdjmVpaYmAgAAkJiZKbSqVComJiQgODtb63vXr16O4uBgvvvjiQz/nzz//xK1bt+Dm5qZTfURERKZCxbQkG53D0syZM9GmTRt4eHigoKAAbdu2RZcuXRASEoLJkyfrXEBMTAyWLl2KVatW4ezZs3jttddQWFiIESNGAACGDRuG2NjYKu9bvnw5BgwYgKeeekqtvaCgAJMmTcKhQ4dw9epVJCYmon///vD29kZkZKTO9RERET2KquzgbZwyngg6rVkSQiAjIwP//e9/ERcXh5MnT6KgoAD+/v5o2bJlrQqIiopCVlYW4uLikJGRAT8/PyQkJEiLvtPS0mBmpp7pzp8/jwMHDmDXrl1VjqdUKvH7779j1apVyM3Nhbu7O3r27Inp06dzryUiInps8Wo4+SiEDivCVCoVrK2tcfr06VqHI1OQn58PBwcH5OXlcf0SERE9kn488ScmrP1Nev7tqCA84+1kxIqMT67f3zqdhjMzM0PLli217llEREREhseJJfnovGZp9uzZmDRpEk6dOiVHPURERFQD3MHbcHTeZ2nYsGEoKiqCr68vLC0tYWNjo/Z6Tk6O3oojIiIizRiWDEfnsDR//nwZyiAiIqK6YFSSj85hKTo6Wo46iIiISAeVwxF38JaPzmuWiIiI6NGjUhm7gscXwxIREZEJqnJvOCPV8SRgWCIiIjJBlcMRF3jLh2GJiIjoMcA1S/LROSy9/PLLuHPnTpX2wsJCvPzyy3opioiIiB6i8r3hmJVko3NYWrVqFe7evVul/e7du1i9erVeiiIiIiLdqBiWZFPjrQPy8/MhhIAQAnfu3IG1tbX0WllZGbZv3w5nZ2dZiiQiIiJ1otLUEtcsyafGYcnR0REKhQIKhQKtWrWq8rpCocC0adP0WhwRERFpVjkbMSrJp8Zhac+ePRBCoFu3btiwYQMaNmwovWZpaQlPT0+4u7vLUiQRERFpxwXe8qlxWAoLCwMApKamwsPDA2ZmvJCOiIjIWLh1gOHofLsTT09PAEBRURHS0tJQUlKi9rqPj49+KiMiIqIaY1aSj85hKSsrCyNGjMCOHTs0vl5WVlbnooiIiEi7yuGIV8PJR+dzaePHj0dubi4OHz4MGxsbJCQkYNWqVWjZsiW2bNkiR41ERERUCa+GMxydZ5b+97//YfPmzQgMDISZmRk8PT3Ro0cP2NvbY9asWejbt68cdRIREVEFVbIRs5JsdJ5ZKiwslPZTatCgAbKysgAAHTp0wPHjx/VbHREREWnEBd6Go3NYat26Nc6fPw8A8PX1xddff43r169j8eLFcHNz03uBREREpIGofBrOSHU8AXQ+DffWW28hPT0dADB16lT06tUL3377LSwtLREfH6/v+oiIiEiDqmfhmJbkonNYevHFF6WvAwICcO3aNZw7dw5NmzaFk5OTXosjIiIizXg1nOHUemfJkpISnD9/HpaWlvjHP/7BoERERGRE3MFbPjqHpaKiIowcORL16tVDu3btkJaWBgB44403MHv2bL0XSERERFVVDkfMSvLROSzFxsbit99+Q1JSEqytraX2iIgIrF27Vq/FERERkWa8Gs5wdF6ztGnTJqxduxadOnWCQqGQ2tu1a4fLly/rtTgiIiLSjGuWDEfnmaWsrCxpn6WKCgsL1cITERERyafK1XCcWZKNzmEpMDAQP/30k/S8PCAtW7YMwcHB+quMiIiIqlU5HPE0nHx0Pg03c+ZM9O7dG2fOnEFpaSkWLFiAM2fO4JdffsHevXvlqJGIiIgeokxl7AoeXzrPLHXu3BkpKSkoLS1Fhw4dsGvXLjg7OyM5ORkBAQFy1EhERESVVF2zxJklueg8swQAXl5eWLp0qb5rISIiohqqvGN3GVd4y6bGYSk/P79G/ezt7WtdDBEREdUOw5J8ahyWHB0dtV7tJoSAQqFAWVmZXgojIiKi6lU+68awJJ8ah6U9e/ZIXwsh0KdPHyxbtgyNGzeWpTAiIiKqXuVoVMY1S7Kp8QLvsLAw6REeHg6lUolOnTqptYeFhdWqiIULF6JZs2awtrZGUFAQjhw5Um3f+Ph4KBQKtUfFncSBB2EuLi4Obm5usLGxQUREBC5evFir2oiIiB5FVRZ4c2ZJNrW+ka6+rF27FjExMZg6dSqOHz8OX19fREZG4ubNm9W+x97eHunp6dLj2rVraq/PnTsX//3vf7F48WIcPnwYtra2iIyMxL179+QeDhERkUFwgbfhGD0sffbZZxg9ejRGjBiBtm3bYvHixahXrx5WrFhR7XsUCgVcXV2lh4uLi/SaEALz58/H5MmT0b9/f/j4+GD16tW4ceMGNm3aZIARERERya/yzFIpw5Js6hSW6np7k5KSEhw7dgwRERF/F2RmhoiICCQnJ1f7voKCAnh6esLDwwP9+/fH6dOnpddSU1ORkZGhdkwHBwcEBQVpPSYREZEp4z5L8qnxAu/nn39e7fm9e/fw6quvwtbWVq1948aNNf7w7OxslJWVqc0MAYCLiwvOnTun8T2tW7fGihUr4OPjg7y8PMybNw8hISE4ffo0mjRpgoyMDOkYlY9Z/lplxcXFKC4ulp7XdJsEIiIiY6l8uxOehpNPjcOSg4OD2vMXX3xR78XURHBwsNo96EJCQvD000/j66+/xvTp02t1zFmzZmHatGn6KpGIiEh23MHbcGocllauXKn3D3dycoJSqURmZqZae2ZmJlxdXWt0DAsLC/j7++PSpUsAIL0vMzMTbm5uasf08/PTeIzY2FjExMRIz/Pz8+Hh4aHLUIiIiIyqtIxhSS5GXeBtaWmJgIAAJCYmSm0qlQqJiYlqs0falJWV4eTJk1Iwat68OVxdXdWOmZ+fj8OHD1d7TCsrK9jb26s9iIiIHmXcZ8lwanVvOH2KiYlBdHQ0AgMD0bFjR8yfPx+FhYUYMWIEAGDYsGFo3LgxZs2aBQD46KOP0KlTJ3h7eyM3NxeffPIJrl27hlGjRgF4sOh8/PjxmDFjBlq2bInmzZtjypQpcHd3x4ABA4w1TCIiIr3iPkuGY/SwFBUVhaysLMTFxSEjIwN+fn5ISEiQFminpaXBzOzvCbDbt29j9OjRyMjIQIMGDRAQEIBffvkFbdu2lfq88847KCwsxCuvvILc3Fx07twZCQkJVTavJCIiMlVV9lliVpKNQlReTk/Iz8+Hg4MD8vLyeEqOiIgeSZ/vvoAFiX/fnaJPB1d8NTTAiBUZn1y/v42+KSURERHprnymw+yvLQ+5dYB8GJaIiIhM0V8nhsz/WqpSpjJmMY83hiUiIiITVD6PZK58MLXEfZbkw7BERERkgsqzkfKv83A8DScfhiUiIiITxrAkP4YlIiIiE1S+dYA5w5LsGJaIiIhMUJXTcFyzJBuGJSIiIhMkLfD+62o47uAtH4YlIiIiE1Q+kVR+NVwpw5JsGJaIiIhMUPmapfLTcNw6QD4MS0RERKaofGaJC7xlx7BERERkgsqjkVLawZthSS4MS0RERCZI/HXazULJmSW5MSwRERGZMDMFw5LcGJaIiIhMUPl6bkvzB7/K76t4J125MCwRERGZoPJ5JKvysFTKmSW5MCwRERGZIGlmSflXWCrjzJJcGJaIiIhMUPk+S+Wn4UoYlmTDsERERGSCymeWyk/DlZQyLMmFYYmIiMiESQu8ObMkG4YlIiIiE1S+z1J5WFIJbh8gF4YlIiIiE1QeiyyVSqmNs0vyYFgiIiIyYeUzSwBQzHVLsmBYIiIiMkF/bx2gkNo4syQPhiUiIiITVL51gEKhkO4Px7AkD4YlIiIiE1Q+s6RQABZK7uItJ4YlIiIiE1QeixRQSGGppKzMeAU9xhiWiIiITFD5zJKZosIu3pxZkgXDEhERkQkq32fJzEzB+8PJjGGJiIjIBKnE37NIXOAtL4YlIiIiE/T3abiKa5YYluTAsERERGSCVBrXLDEsyYFhiYiIyASVr1lS2zqgjAu85cCwREREZILKY5GZggu85cawREREZIJUosIO3uZc4C0nhiUiIiITVL5mSQFIM0u8ka48HomwtHDhQjRr1gzW1tYICgrCkSNHqu27dOlShIaGokGDBmjQoAEiIiKq9B8+fDgUCoXao1evXnIPg4iIyGCkfZbU1iwxLMnB6GFp7dq1iImJwdSpU3H8+HH4+voiMjISN2/e1Ng/KSkJQ4YMwZ49e5CcnAwPDw/07NkT169fV+vXq1cvpKenS4/vv//eEMMhIiIyCGnrADMFLMzL7w3HsCQHo4elzz77DKNHj8aIESPQtm1bLF68GPXq1cOKFSs09v/222/x+uuvw8/PD23atMGyZcugUqmQmJio1s/Kygqurq7So0GDBoYYDhERkUFIa5aACgu8eTWcHIwalkpKSnDs2DFERERIbWZmZoiIiEBycnKNjlFUVIT79++jYcOGau1JSUlwdnZG69at8dprr+HWrVvVHqO4uBj5+flqDyIiokdZ+cySosLVcNyUUh5GDUvZ2dkoKyuDi4uLWruLiwsyMjJqdIx3330X7u7uaoGrV69eWL16NRITEzFnzhzs3bsXvXv3Rlk1d2OeNWsWHBwcpIeHh0ftB0VERGQAqor7LP11NRw3pZSHubELqIvZs2djzZo1SEpKgrW1tdQ+ePBg6esOHTrAx8cHXl5eSEpKQvfu3ascJzY2FjExMdLz/Px8BiYiInqkqTTc7oQLvOVh1JklJycnKJVKZGZmqrVnZmbC1dVV63vnzZuH2bNnY9euXfDx8dHat0WLFnBycsKlS5c0vm5lZQV7e3u1BxER0aPt76vhuCmlvIwaliwtLREQEKC2OLt8sXZwcHC175s7dy6mT5+OhIQEBAYGPvRz/vzzT9y6dQtubm56qZuIiMjY/t5nSSHdG44LvOVh9KvhYmJisHTpUqxatQpnz57Fa6+9hsLCQowYMQIAMGzYMMTGxkr958yZgylTpmDFihVo1qwZMjIykJGRgYKCAgBAQUEBJk2ahEOHDuHq1atITExE//794e3tjcjISKOMkYiISN803RuOm1LKw+hrlqKiopCVlYW4uDhkZGTAz88PCQkJ0qLvtLQ0mJn9nekWLVqEkpIS/Pvf/1Y7ztSpU/Hhhx9CqVTi999/x6pVq5Cbmwt3d3f07NkT06dPh5WVlUHHRkREJBdNa5a4wFseRg9LADBu3DiMGzdO42tJSUlqz69evar1WDY2Nti5c6eeKiMiIno0Vbwarr71g1/nhcWlxizpsWX003BERERUe2YKBez/Ckv59+4buZrHE8MSERGRCao4s2RvbQGAYUkuDEtEREQmSPXX8iSFQgF7mwczS3fu8TScHBiWiIiITJCosM+SXfnM0l3OLMmBYYmIiMgEVbwa7u/TcKXSlgKkPwxLREREJkjaZwmQTsOVqQTu3td8H1SqPYYlIiIiE1Q+gaRQKGBjoYTS7MHNdPPvct2SvjEsERERmaDyq+HMFH8t8ub2AbJhWCIiIjJBqgozSwBgb/Ng3dIdhiW9Y1giIiIyQeXLuBV//deufGaJp+H0jmGJiIjIBKn+mlpSKv+aWeLGlLJhWCIiIjJBpeVhSVE5LHFmSd8YloiIiEyQNLP011Vwf5+G48ySvjEsERERmaAyoR6Wyhd48zSc/jEsERERmaCySjNL0mk4LvDWO4YlIiIiE1QelswU6qfhuHWA/jEsERERmaAqM0s2XOAtF4YlIiIiE1Qelsyl03Bc4C0XhiUiIiITVCYqn4bjDt5yYVgiIiIyQZW3DrC3Kb83HE/D6RvDEhERkQmqsnWAdDUcZ5b0jWGJiIjIBJWVaQ5LxaUqFJeWGa2uxxHDEhERkQmSZpb+WrNU/68F3gBwh6fi9IphiYiIyARJ+yz99ZtcaaaAnRWviJMDwxIREZEJqrzPEgA0srcCAKRmFxqlpscVwxIREZGJKVMJlP4VlqzMlVJ7pxZPAQD2X8w2Sl2PK4YlIiIiE3Pv/t8LuG0s/g5Lod5OAID9F7MMXtPjjGGJiIjIxNytEJasLf7+VR7i5QQzBXA5qxA3cu8ao7THEsMSERGRiblb8iAs2VgooVD8vWbJoZ4FfJo4AgAO8FSc3jAsERERmZjymSUbS2WV10Jb/nUq7hLDkr4wLBEREZmYijNLlYW2bAQAOHgpW7olCtUNwxIREZGJKZ9ZqrheqZx/U0fYWiqRU1iCM+n5hi7tscSwREREZGK0nYazUJoh2ItbCOgTwxIREZGJuffXabh6FuYaX+/81xYCBy5xCwF9YFgiIiIyMYV/hSUrDafhAKDzX+uWfk29La1votpjWCIiIjIxN+/cAwA0qm+l8XWvRrZwd7BGSZkK83++ACG40LsuHomwtHDhQjRr1gzW1tYICgrCkSNHtPZfv3492rRpA2tra3To0AHbt29Xe10Igbi4OLi5ucHGxgYRERG4ePGinEMgIiIymPTcB2HJ3dFG4+sKhQKvhXsBAL7edwXj16aguJQzTLVl9LC0du1axMTEYOrUqTh+/Dh8fX0RGRmJmzdvauz/yy+/YMiQIRg5ciROnDiBAQMGYMCAATh16pTUZ+7cufjvf/+LxYsX4/Dhw7C1tUVkZCTu3btnqGERERHJJj3vwe7cbo7W1fZ5KbgZ5v7bB+ZmCmxOuYGXlh/Blt9u4Gx6PoOTjhTCyHNzQUFB+Oc//4kvv/wSAKBSqeDh4YE33ngD7733XpX+UVFRKCwsxLZt26S2Tp06wc/PD4sXL4YQAu7u7nj77bcxceJEAEBeXh5cXFwQHx+PwYMHP7Sm/Px8ODg44MzVdNjZ2+tppERERLVX/tu6VCXQ/8sDyL9Xiu9GByHEy0nr+/ZfzMJr/3ccBcWlUpvSTAHPhvXQ0qU+vJ3ro6GtFawtzGBjoYSNhRLWlg/+a6FUQKFQwEyhgJkCUEABhQIPnps9eG6mwF99HvxXUeGzK2wujoqvVGyvjerefyc/H22buSEvLw/2evz9rXkZvYGUlJTg2LFjiI2NldrMzMwQERGB5ORkje9JTk5GTEyMWltkZCQ2bdoEAEhNTUVGRgYiIiKk1x0cHBAUFITk5GSNYam4uBjFxcXS8/z8B/tSRM7fDzOrerUeHxERkVzsrM0R6Nnwof1CWzbCj6+HYMXBq7iQeQcXMu/gzr1SXMkuxJXsQuw8nWmAag1DVVwky3GNGpays7NRVlYGFxcXtXYXFxecO3dO43syMjI09s/IyJBeL2+rrk9ls2bNwrRp06q0W5qbQWlu9DOVREREACDN6jxV3xKT+7aFZQ1/R7V0scOs5zsAeLCu9+adYlzIvIOLmQW4nFWAguJS3C0pw937Zbh3/8F/75aUoVQlIASgEn//98HjwUyXqPC8vE+5iieuKp7CUusD3U9uaTsfVlYmz+9so4alR0VsbKzabFV+fj48PDxwfEoPvU7jERERGZtCoYCLvTVc7K2lW6M8LvLz8+EwT//HNeq0iZOTE5RKJTIz1acAMzMz4erqqvE9rq6uWvuX/1eXY1pZWcHe3l7tQURERAQYOSxZWloiICAAiYmJUptKpUJiYiKCg4M1vic4OFitPwDs3r1b6t+8eXO4urqq9cnPz8fhw4erPSYRERFRdYx+Gi4mJgbR0dEIDAxEx44dMX/+fBQWFmLEiBEAgGHDhqFx48aYNWsWAOCtt95CWFgYPv30U/Tt2xdr1qzB0aNHsWTJEgAPphfHjx+PGTNmoGXLlmjevDmmTJkCd3d3DBgwwFjDJCIiIhNl9LAUFRWFrKwsxMXFISMjA35+fkhISJAWaKelpcHM7O8JsJCQEHz33XeYPHky3n//fbRs2RKbNm1C+/btpT7vvPMOCgsL8corryA3NxedO3dGQkICrK2r34+CiIiISBOj77P0KCrfZ0nf+zQQERGRfOT6/c3r4omIiIi0YFgiIiIi0oJhiYiIiEgLhiUiIiIiLRiWiIiIiLRgWCIiIiLSgmGJiIiISAuGJSIiIiItGJaIiIiItDD67U4eReWbmufn5xu5EiIiIqqp8t/b+r45CcOSBrdu3QIAeHh4GLkSIiIi0tWtW7fg4OCgt+MxLGnQsGFDAA9u4qvPb/ajLj8/Hx4eHvjjjz+eqHvicdwc95OA4+a4nwR5eXlo2rSp9HtcXxiWNDAze7CUy8HB4Yn6Q1bO3t6e436CcNxPFo77yfKkjrv897jejqfXoxERERE9ZhiWiIiIiLRgWNLAysoKU6dOhZWVlbFLMSiOm+N+EnDcHPeTgOPW77gVQt/X1xERERE9RjizRERERKQFwxIRERGRFgxLRERERFowLBERERFp8cSGpYULF6JZs2awtrZGUFAQjhw5orX/+vXr0aZNG1hbW6NDhw7Yvn27gSrVL13Gffr0afzrX/9Cs2bNoFAoMH/+fMMVqme6jHvp0qUIDQ1FgwYN0KBBA0RERDz0z8ejSpdxb9y4EYGBgXB0dIStrS38/PzwzTffGLBa/dH173e5NWvWQKFQYMCAAfIWKBNdxh0fHw+FQqH2sLa2NmC1+qPrzzs3Nxdjx46Fm5sbrKys0KpVK5P8f7ou4w4PD6/y81YoFOjbt68BK9YPXX/e8+fPR+vWrWFjYwMPDw9MmDAB9+7d0+1DxRNozZo1wtLSUqxYsUKcPn1ajB49Wjg6OorMzEyN/Q8ePCiUSqWYO3euOHPmjJg8ebKwsLAQJ0+eNHDldaPruI8cOSImTpwovv/+e+Hq6io+//xzwxasJ7qO+z//+Y9YuHChOHHihDh79qwYPny4cHBwEH/++aeBK68bXce9Z88esXHjRnHmzBlx6dIlMX/+fKFUKkVCQoKBK68bXcddLjU1VTRu3FiEhoaK/v37G6ZYPdJ13CtXrhT29vYiPT1demRkZBi46rrTddzFxcUiMDBQ9OnTRxw4cECkpqaKpKQkkZKSYuDK60bXcd+6dUvtZ33q1CmhVCrFypUrDVt4Hek67m+//VZYWVmJb7/9VqSmpoqdO3cKNzc3MWHCBJ0+94kMSx07dhRjx46VnpeVlQl3d3cxa9Ysjf0HDRok+vbtq9YWFBQkxowZI2ud+qbruCvy9PQ02bBUl3ELIURpaamws7MTq1atkqtEWdR13EII4e/vLyZPnixHebKpzbhLS0tFSEiIWLZsmYiOjjbJsKTruFeuXCkcHBwMVJ18dB33okWLRIsWLURJSYmhSpRFXf9+f/7558LOzk4UFBTIVaIsdB332LFjRbdu3dTaYmJixDPPPKPT5z5xp+FKSkpw7NgxRERESG1mZmaIiIhAcnKyxvckJyer9QeAyMjIavs/imoz7seBPsZdVFSE+/fv6/3GjHKq67iFEEhMTMT58+fRpUsXOUvVq9qO+6OPPoKzszNGjhxpiDL1rrbjLigogKenJzw8PNC/f3+cPn3aEOXqTW3GvWXLFgQHB2Ps2LFwcXFB+/btMXPmTJSVlRmq7DrTx//Xli9fjsGDB8PW1lauMvWuNuMOCQnBsWPHpFN1V65cwfbt29GnTx+dPvuJu5FudnY2ysrK4OLiotbu4uKCc+fOaXxPRkaGxv4ZGRmy1alvtRn340Af43733Xfh7u5eJTA/ymo77ry8PDRu3BjFxcVQKpX46quv0KNHD7nL1ZvajPvAgQNYvnw5UlJSDFChPGoz7tatW2PFihXw8fFBXl4e5s2bh5CQEJw+fRpNmjQxRNl1VptxX7lyBf/73/8wdOhQbN++HZcuXcLrr7+O+/fvY+rUqYYou87q+v+1I0eO4NSpU1i+fLlcJcqiNuP+z3/+g+zsbHTu3BlCCJSWluLVV1/F+++/r9NnP3FhiUgXs2fPxpo1a5CUlGSyi191YWdnh5SUFBQUFCAxMRExMTFo0aIFwsPDjV2aLO7cuYOXXnoJS5cuhZOTk7HLMajg4GAEBwdLz0NCQvD000/j66+/xvTp041YmbxUKhWcnZ2xZMkSKJVKBAQE4Pr16/jkk09MJizV1fLly9GhQwd07NjR2KXILikpCTNnzsRXX32FoKAgXLp0CW+99RamT5+OKVOm1Pg4T1xYcnJyglKpRGZmplp7ZmYmXF1dNb7H1dVVp/6PotqM+3FQl3HPmzcPs2fPxs8//wwfHx85y9S72o7bzMwM3t7eAAA/Pz+cPXsWs2bNMpmwpOu4L1++jKtXr6Jfv35Sm0qlAgCYm5vj/Pnz8PLykrdoPdDH328LCwv4+/vj0qVLcpQoi9qM283NDRYWFlAqlVLb008/jYyMDJSUlMDS0lLWmvWhLj/vwsJCrFmzBh999JGcJcqiNuOeMmUKXnrpJYwaNQoA0KFDBxQWFuKVV17BBx98ADOzmq1GeuLWLFlaWiIgIACJiYlSm0qlQmJiotq/sioKDg5W6w8Au3fvrrb/o6g2434c1Hbcc+fOxfTp05GQkIDAwEBDlKpX+vp5q1QqFBcXy1GiLHQdd5s2bXDy5EmkpKRIj+eeew5du3ZFSkoKPDw8DFl+renj511WVoaTJ0/Czc1NrjL1rjbjfuaZZ3Dp0iUpFAPAhQsX4ObmZhJBCajbz3v9+vUoLi7Giy++KHeZelebcRcVFVUJROVBWehya1wdF6I/FtasWSOsrKxEfHy8OHPmjHjllVeEo6OjdNnsSy+9JN577z2p/8GDB4W5ubmYN2+eOHv2rJg6darJbh2gy7iLi4vFiRMnxIkTJ4Sbm5uYOHGiOHHihLh48aKxhlAruo579uzZwtLSUvzwww9ql9reuXPHWEOoFV3HPXPmTLFr1y5x+fJlcebMGTFv3jxhbm4uli5daqwh1Iqu467MVK+G03Xc06ZNEzt37hSXL18Wx44dE4MHDxbW1tbi9OnTxhpCreg67rS0NGFnZyfGjRsnzp8/L7Zt2yacnZ3FjBkzjDWEWqntn/POnTuLqKgoQ5erN7qOe+rUqcLOzk58//334sqVK2LXrl3Cy8tLDBo0SKfPfSLDkhBCfPHFF6Jp06bC0tJSdOzYURw6dEh6LSwsTERHR6v1X7dunWjVqpWwtLQU7dq1Ez/99JOBK9YPXcadmpoqAFR5hIWFGb7wOtJl3J6enhrHPXXqVMMXXke6jPuDDz4Q3t7ewtraWjRo0EAEBweLNWvWGKHqutP173dFphqWhNBt3OPHj5f6uri4iD59+ojjx48boeq60/Xn/csvv4igoCBhZWUlWrRoIT7++GNRWlpq4KrrTtdxnzt3TgAQu3btMnCl+qXLuO/fvy8+/PBD4eXlJaytrYWHh4d4/fXXxe3bt3X6TIUQusxDERERET1Znrg1S0RERES6YFgiIiIi0oJhiYiIiEgLhiUiIiIiLRiWiIiIiLRgWCIiIiLSgmGJiIiISAuGJSIiIiItGJaI6JGTlJQEhUKB3NzcavvEx8fD0dHxocdSKBTYtGmTzjWcP38erq6uuHPnjs7vrakzZ86gSZMmKCwslO0ziKjuGJaISDaLFy+GnZ0dSktLpbaCggJYWFggPDxcrW95QLp8+TJCQkKQnp4OBweHGn/Whx9+CD8/Pz1VDsTGxuKNN96AnZ2d3o5ZWdu2bdGpUyd89tlnsn0GEdUdwxIRyaZr164oKCjA0aNHpbb9+/fD1dUVhw8fxr1796T2PXv2oGnTpvDy8oKlpSVcXV2hUCiMUTbS0tKwbds2DB8+XPbPGjFiBBYtWqQWKIno0cKwRESyad26Ndzc3JCUlCS1JSUloX///mjevDkOHTqk1t61a1fp68qn4eLj49G0aVPUq1cPAwcOxK1bt9RemzZtGn777TcoFAooFArEx8dLr2dnZ2PgwIGoV68eWrZsiS1btmite926dfD19UXjxo3V2g8ePIjw8HDUq1cPDRo0QGRkJG7fvg0ACA8PxxtvvIHx48ejQYMGcHFxwdKlS1FYWIgRI0bAzs4O3t7e2LFjh9oxe/TogZycHOzdu7dG31MiMjyGJSKSVdeuXbFnzx7p+Z49exAeHo6wsDCp/e7duzh8+LAUlio7fPgwRo4ciXHjxiElJQVdu3bFjBkzpNejoqLw9ttvo127dkhPT0d6ejqioqKk16dNm4ZBgwbh999/R58+fTB06FDk5ORUW/P+/fsRGBio1paSkoLu3bujbdu2SE5OxoEDB9CvXz+UlZVJfVatWgUnJyccOXIEb7zxBl577TW88MILCAkJwfHjx9GzZ0+89NJLKCoqkt5jaWkJPz8/7N+/v4bfUSIyOEFEJKOlS5cKW1tbcf/+fZGfny/Mzc3FzZs3xXfffSe6dOkihBAiMTFRABDXrl0TQgixZ88eAUDcvn1bCCHEkCFDRJ8+fdSOGxUVJRwcHKTnU6dOFb6+vlU+H4CYPHmy9LygoEAAEDt27Ki2Zl9fX/HRRx+ptQ0ZMkQ888wz1b4nLCxMdO7cWXpeWloqbG1txUsvvSS1paenCwAiOTlZ7b0DBw4Uw4cPr/bYRGRcnFkiIlmFh4ejsLAQv/76K/bv349WrVqhUaNGCAsLk9YtJSUloUWLFmjatKnGY5w9exZBQUFqbcHBwTWuwcfHR/ra1tYW9vb2uHnzZrX97969C2tra7W28pmlmn6OUqnEU089hQ4dOkhtLi4uAFDls21sbNRmm4jo0WJu7AKI6PHm7e2NJk2aYM+ePbh9+zbCwsIAAO7u7vDw8MAvv/yCPXv2oFu3brLVYGFhofZcoVBApVJV29/JyUlai1TOxsamVp9Tsa18wXrlz87JyYGXl9dDj09ExsGZJSKSXdeuXZGUlISkpCS1LQO6dOmCHTt24MiRI9WuVwKAp59+GocPH1Zrq7g4HHiw9qfi+qG68Pf3x5kzZ9TafHx8kJiYqJfjV3bq1Cn4+/vLcmwiqjuGJSKSXdeuXXHgwAGkpKRIM0sAEBYWhq+//holJSVaw9Kbb76JhIQEzJs3DxcvXsSXX36JhIQEtT7NmjVDamoqUlJSkJ2djeLi4lrXGxkZieTkZLXwFRsbi19//RWvv/46fv/9d5w7dw6LFi1CdnZ2rT8HAK5evYrr168jIiKiTschIvkwLBGR7Lp27Yq7d+/C29tbWrcDPAhLd+7ckbYYqE6nTp2wdOlSLFiwAL6+vti1axcmT56s1udf//oXevXqha5du6JRo0b4/vvva11v7969YW5ujp9//llqa9WqFXbt2oXffvsNHTt2RHBwMDZv3gxz87qtZvj+++/Rs2dPeHp61uk4RCQfhRBCGLsIIqJHzcKFC7Flyxbs3LlTts8oKSlBy5Yt8d133+GZZ56R7XOIqG64wJuISIMxY8YgNzcXd+7cke2WJ2lpaXj//fcZlIgecZxZIiIiItKCa5aIiIiItGBYIiIiItKCYYmIiIhIC4YlIiIiIi0YloiIiIi0YFgiIiIi0oJhiYiIiEgLhiUiIiIiLRiWiIiIiLT4fwBZhMEhlIoBAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(flame.grid * 100, flame.heat_release_rate,label=\"Temperature\")\n",
    "plt.title('Heat release rate vs width (cm)')\n",
    "plt.xlabel('Width (cm)')\n",
    "plt.ylabel('Heat release rate (J/s)')\n",
    "plt.xlim([0, 0.8])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8c10f953",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nplt.figure(3)\\nplt.plot(actual_time_scale, flame.T, \"-o\")\\nplt.xlabel(\"Time (s)\")\\nplt.ylabel(\"Temperature (K)\");\\n\\n# Create a plot\\n\\nplt.plot(mixture_fraction_points[0], flame_temp[0][0],label = \\'Vel of air - 0.1 m/s\\')\\nplt.plot(mixture_fraction_points[1], flame_temp[1][0],label = \\'Vel of air - 2.0 m/s\\')\\nplt.plot(mixture_fraction_points[2], flame_temp[2][0],label = \\'Vel of air - 3.3 m/s\\')\\n\\n# Add labels to the axes and a title\\nplt.xlabel(\\'Mixture fraction (Z)\\')\\nplt.ylabel(\\'Temperature (K)\\')\\nplt.title(\\'Diffusion Flame Temperature vs. Mixture fraction\\')\\nplt.legend(loc=\\'upper right\\')\\nax1.legend(lines + lines2, labels + labels2, loc=\\'upper right\\', bbox_to_anchor=(1.6, 1))\\n# Enable the minor grid\\nplt.minorticks_on()\\nplt.grid(True)\\n\\n# Display the plot (you may not need this in some Python environments)\\nplt.show()\\n\\nfig, ax1 = plt.subplots()\\n\\nax1.set_xlabel(\\'Mixture fraction (Z)\\')\\nax1.set_ylabel(\\'Mass Fractions_O2\\', color=\\'tab:blue\\')\\nax1.plot(mixture_fraction_points[0], YO2_values[0], label=\\'O2_Vel of air - 0.1 m/s\\')\\nax1.plot(mixture_fraction_points[1], YO2_values[1], label=\\'O2_Vel of air - 2.0 m/s\\')\\nax1.plot(mixture_fraction_points[2], YO2_values[2], label=\\'O2_Vel of air - 3.3 m/s\\')\\nax1.tick_params(axis=\\'y\\', labelcolor=\\'tab:blue\\')\\n\\nax2 = ax1.twinx()\\nax2.set_ylabel(\\'Mass Fractions_C8H18\\', color=\\'tab:red\\')\\nax2.plot(mixture_fraction_points[0], YC8H18_values[0], label=\\'C8H18_Vel of air - 0.1 m/s\\',linestyle = \\'--\\')\\nax2.plot(mixture_fraction_points[1], YC8H18_values[1], label=\\'C8H18_Vel of air - 2.0 m/s\\',linestyle = \\'--\\')\\nax2.plot(mixture_fraction_points[2], YC8H18_values[2], label=\\'C8H18_Vel of air - 3.3 m/s\\',linestyle = \\'--\\')\\nax2.tick_params(axis=\\'y\\', labelcolor=\\'tab:red\\')\\n\\nfig.tight_layout()\\n\\n# Add legends\\nlines, labels = ax1.get_legend_handles_labels()\\nlines2, labels2 = ax2.get_legend_handles_labels()\\nax1.legend(lines + lines2, labels + labels2, loc=\\'upper right\\', bbox_to_anchor=(1.6, 1))\\n\\nplt.title(\\'Mass fractions of Species vs. Mixture fraction (Z)\\')\\nplt.show()\\n'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "plt.figure(3)\n",
    "plt.plot(actual_time_scale, flame.T, \"-o\")\n",
    "plt.xlabel(\"Time (s)\")\n",
    "plt.ylabel(\"Temperature (K)\");\n",
    "\n",
    "# Create a plot\n",
    "\n",
    "plt.plot(mixture_fraction_points[0], flame_temp[0][0],label = 'Vel of air - 0.1 m/s')\n",
    "plt.plot(mixture_fraction_points[1], flame_temp[1][0],label = 'Vel of air - 2.0 m/s')\n",
    "plt.plot(mixture_fraction_points[2], flame_temp[2][0],label = 'Vel of air - 3.3 m/s')\n",
    "\n",
    "# Add labels to the axes and a title\n",
    "plt.xlabel('Mixture fraction (Z)')\n",
    "plt.ylabel('Temperature (K)')\n",
    "plt.title('Diffusion Flame Temperature vs. Mixture fraction')\n",
    "plt.legend(loc='upper right')\n",
    "ax1.legend(lines + lines2, labels + labels2, loc='upper right', bbox_to_anchor=(1.6, 1))\n",
    "# Enable the minor grid\n",
    "plt.minorticks_on()\n",
    "plt.grid(True)\n",
    "\n",
    "# Display the plot (you may not need this in some Python environments)\n",
    "plt.show()\n",
    "\n",
    "fig, ax1 = plt.subplots()\n",
    "\n",
    "ax1.set_xlabel('Mixture fraction (Z)')\n",
    "ax1.set_ylabel('Mass Fractions_O2', color='tab:blue')\n",
    "ax1.plot(mixture_fraction_points[0], YO2_values[0], label='O2_Vel of air - 0.1 m/s')\n",
    "ax1.plot(mixture_fraction_points[1], YO2_values[1], label='O2_Vel of air - 2.0 m/s')\n",
    "ax1.plot(mixture_fraction_points[2], YO2_values[2], label='O2_Vel of air - 3.3 m/s')\n",
    "ax1.tick_params(axis='y', labelcolor='tab:blue')\n",
    "\n",
    "ax2 = ax1.twinx()\n",
    "ax2.set_ylabel('Mass Fractions_C8H18', color='tab:red')\n",
    "ax2.plot(mixture_fraction_points[0], YC8H18_values[0], label='C8H18_Vel of air - 0.1 m/s',linestyle = '--')\n",
    "ax2.plot(mixture_fraction_points[1], YC8H18_values[1], label='C8H18_Vel of air - 2.0 m/s',linestyle = '--')\n",
    "ax2.plot(mixture_fraction_points[2], YC8H18_values[2], label='C8H18_Vel of air - 3.3 m/s',linestyle = '--')\n",
    "ax2.tick_params(axis='y', labelcolor='tab:red')\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "# Add legends\n",
    "lines, labels = ax1.get_legend_handles_labels()\n",
    "lines2, labels2 = ax2.get_legend_handles_labels()\n",
    "ax1.legend(lines + lines2, labels + labels2, loc='upper right', bbox_to_anchor=(1.6, 1))\n",
    "\n",
    "plt.title('Mass fractions of Species vs. Mixture fraction (Z)')\n",
    "plt.show()\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7353d983",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Extract concentration data\n",
    "X_C8H18 = flame.X[63]\n",
    "X_O2 = flame.X[3]\n",
    "X_CO = flame.X[12]\n",
    "X_NO = flame.X[104]\n",
    "X_CO2 = flame.X[14]\n",
    "X_H2O = flame.X[8]\n",
    "\n",
    "fig, ax1 = plt.subplots()\n",
    "\n",
    "ax1.set_xlabel('Time (s)')\n",
    "ax1.set_ylabel('Temperature (K)', color='tab:blue')\n",
    "ax1.plot(actual_time_scale, flame.T,label=\"Temperature\")\n",
    "\n",
    "ax1.tick_params(axis='y', labelcolor='tab:blue')\n",
    "\n",
    "ax2 = ax1.twinx()\n",
    "ax2.set_ylabel('Mass Fractions', color='tab:red')\n",
    "plt.plot(actual_time_scale, X_C8H18, label=\"$C_{8}H_{18}$\",linestyle = '--')\n",
    "plt.plot(actual_time_scale, X_CO2, label=\"$CO_{2}$\",linestyle = '--')\n",
    "plt.plot(actual_time_scale, X_H2O, label=\"$H_{2}O$\",linestyle = '--')\n",
    "plt.plot(actual_time_scale, X_O2, label=\"$O_{2}$\",linestyle = '--')\n",
    "plt.plot(actual_time_scale, X_CO, label=\"$CO$\",linestyle = '--')\n",
    "plt.plot(actual_time_scale, X_NO, label=\"$NO$\",linestyle = '--')\n",
    "ax2.tick_params(axis='y', labelcolor='tab:red')\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "# Add legends\n",
    "lines1, labels = ax1.get_legend_handles_labels()\n",
    "lines2, labels2 = ax2.get_legend_handles_labels()\n",
    "ax2.legend(lines1 + lines2, labels + labels2, loc='upper right', bbox_to_anchor=(1.45, 1))\n",
    "plt.xlim([-0.004, 0.004])\n",
    "\n",
    "plt.title('Flame properties vs. Time (s)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f4e79f6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots()\n",
    "\n",
    "ax1.set_xlabel('Time (s)')\n",
    "ax1.set_ylabel('Velocity (m/s)', color='tab:blue')\n",
    "ax1.plot(actual_time_scale, velocity,label=\"velocity\")\n",
    "\n",
    "ax1.tick_params(axis='y', labelcolor='tab:blue')\n",
    "\n",
    "ax2 = ax1.twinx()\n",
    "ax2.set_ylabel('Heat release rate (J/s)', color='tab:red')\n",
    "plt.plot(actual_time_scale, flame.heat_release_rate,color='tab:red',label = \"heat release rate\")\n",
    "ax2.tick_params(axis='y', labelcolor='tab:red')\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "# Add legends\n",
    "lines1, labels = ax1.get_legend_handles_labels()\n",
    "lines2, labels2 = ax2.get_legend_handles_labels()\n",
    "ax2.legend(lines1 + lines2, labels + labels2, loc='upper right', bbox_to_anchor=(1.45, 1))\n",
    "plt.xlim([-0.0035, 0.003])\n",
    "\n",
    "plt.title('Flame properties vs. Time (s)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "659cab97",
   "metadata": {},
   "source": [
    "Question 5 - Temperature and Heat release rate "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "8eeb5a2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots()\n",
    "\n",
    "ax1.set_xlabel('Time (s)')\n",
    "ax1.set_ylabel('Temperature (K)', color='tab:blue')\n",
    "ax1.plot(actual_time_scale, flame.T,label=\"Temperature\")\n",
    "\n",
    "ax1.tick_params(axis='y', labelcolor='tab:blue')\n",
    "\n",
    "ax2 = ax1.twinx()\n",
    "ax2.set_ylabel('Heat release rate (J/s)', color='tab:red')\n",
    "plt.plot(actual_time_scale, flame.heat_release_rate,color='tab:red',label = \"heat release rate\")\n",
    "ax2.tick_params(axis='y', labelcolor='tab:red')\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "# Add legends\n",
    "lines1, labels = ax1.get_legend_handles_labels()\n",
    "lines2, labels2 = ax2.get_legend_handles_labels()\n",
    "ax2.legend(lines1 + lines2, labels + labels2, loc='upper right', bbox_to_anchor=(1.45, 1))\n",
    "plt.xlim([-0.0035, 0.003])\n",
    "\n",
    "plt.title('Flame properties vs. Time (s)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ece0370",
   "metadata": {},
   "source": [
    "Question 6 - Species production rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c68e8e7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Specify the species for which you want to calculate production rates\n",
    "species_of_interest = ['IXC8H18', 'O2', 'CO', 'NO', 'CO2', 'H2O']  # Replace with your desired species\n",
    "\n",
    "# Calculate the production rates\n",
    "production_rates = flame.net_production_rates  # All species' production rates\n",
    "production_rates_species = {species: production_rates[gas.species_index(species)] for species in species_of_interest}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a872690a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.0005, 0.001)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a plot\n",
    "\n",
    "plt.plot(actual_time_scale, production_rates_species['IXC8H18'], label=\"$C_{8}H_{18}$\")\n",
    "plt.plot(actual_time_scale, production_rates_species['O2'], label=\"$O_{2}$\")\n",
    "plt.plot(actual_time_scale, production_rates_species['CO'], label=\"$CO$\")\n",
    "plt.plot(actual_time_scale, production_rates_species['NO'], label=\"$NO$\")\n",
    "plt.plot(actual_time_scale, production_rates_species['CO2'], label=\"$CO_{2}$\")\n",
    "plt.plot(actual_time_scale, production_rates_species['H2O'], label=\"$H_{2}O$\")\n",
    "\n",
    "plt.xlabel(\"Time (s)\")\n",
    "plt.ylabel(\"Species Production rates\");\n",
    "plt.title('Species Production rates vs. Time')\n",
    "plt.legend(loc='upper right', bbox_to_anchor=(1.2, 1))\n",
    "plt.xlim([-0.0005, 0.001])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98b99d6e",
   "metadata": {},
   "source": [
    "Question 8 - Varying the phi - 0.6 , 0.65, 0.7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7db201d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "Phi = [0.6,0.65,0.7]\n",
    "NO_Y_values = []\n",
    "CO_Y_values = []\n",
    "final_temp_values = []\n",
    "grid_values = []\n",
    "max_heat_release_rate = []\n",
    "flame_velocity_values = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "940f4d96",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "*********** Solving on 100 point grid with energy equation enabled ***********\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     4.005e-06       6.09\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     3.041e-05      6.071\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     7.697e-05      5.685\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     0.0002598      5.079\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps      0.001973      4.542\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps       0.00749      3.348\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [100] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "grid refinement disabled.\n",
      "\n",
      "******************** Solving with grid refinement enabled ********************\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [100] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "##############################################################################\n",
      "Refining grid in flame.\n",
      "    New points inserted after grid points 33 34 35 36 37 38 \n",
      "    to resolve AXC5H10 C2H C2H2 C2H3 C2H3CHO C2H4 C2H5 C2H5O2 C2H6 C3H3 C3H4XA C3H4XP C3H5O C3H5XA C3H5XT C3H6 C3H8 C4H10 C4H6 C4H7 C4H8X1 C5H10X1 C6H12X1 CH2 CH2CCH2OH CH2CHO CH2CO CH2OH CH3 CH3CHO CH3O CH3O2 CH3OH CH4 CXC8H17 H2 HCCO HCNO HCO HNCO HO2 HOCHO IXC3H5CHO IXC3H6CO IXC3H7 IXC3H7O2 IXC4H10 IXC4H6OH IXC4H7 IXC4H8 IXC4H9 IXC4H9O2 IXC5H9 NXC3H7O2 TXC4H9 TXC4H9O2 XXC7H13 YXC7H14 YXC7H15 \n",
      "##############################################################################\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     0.0002563      4.199\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [106] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "##############################################################################\n",
      "Refining grid in flame.\n",
      "    New points inserted after grid points 35 36 37 38 39 40 41 \n",
      "    to resolve C2H3 C2H4 C3H2 C3H4XA C3H4XP C3H5XA C3H5XT C4H6 CH2 CH2GSG CH2OH CH3 CH3O CH3O2 HCCO HCO IXC3H6CO IXC3H7 IXC3H7O2 IXC4H7 IXC4H9 IXC4H9O2 TXC4H9 YXC7H14 \n",
      "##############################################################################\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     0.0003844      3.648\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [113] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "##############################################################################\n",
      "Refining grid in flame.\n",
      "    New points inserted after grid points 39 40 \n",
      "    to resolve IXC3H7 \n",
      "##############################################################################\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [115] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "no new points needed in flame\n",
      "\n",
      "*********** Solving on 100 point grid with energy equation enabled ***********\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     4.005e-06      6.313\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     2.027e-05      6.253\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     5.132e-05      5.943\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     0.0001732      5.585\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps      0.001315      4.547\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps      0.003745       3.84\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps       0.09598    -0.7314\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [100] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "grid refinement disabled.\n",
      "\n",
      "******************** Solving with grid refinement enabled ********************\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [100] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "##############################################################################\n",
      "Refining grid in flame.\n",
      "    New points inserted after grid points 33 34 35 36 37 \n",
      "    to resolve AXC5H10 C2H C2H2 C2H3 C2H3CHO C2H4 C2H5 C2H5O2 C2H6 C3H2 C3H3 C3H4XA C3H4XP C3H5O C3H5XA C3H5XT C3H6 C3H8 C4H10 C4H6 C4H7 C4H8X1 C5H10X1 C5H9 C6H12X1 CH CH2 CH2CHO CH2CO CH2GSG CH2OH CH3 CH3CHO CH3COCH2 CH3COCH3 CH3O CH3O2 CH3OH CH4 CXC8H17 H2 HCCO HCNO HCO HO2 HOCHO IXC3H5CHO IXC3H5CO IXC3H6CO IXC3H7 IXC3H7O2 IXC4H10 IXC4H6OH IXC4H7 IXC4H7OH IXC4H8 IXC4H9 IXC4H9O2 IXC5H9 IXC8H18 NXC3H7 NXC3H7O2 O OH TXC4H9 TXC4H9O2 YXC7H14 \n",
      "##############################################################################\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     0.0002563      4.062\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [105] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "##############################################################################\n",
      "Refining grid in flame.\n",
      "    New points inserted after grid points 34 35 36 37 38 39 40 \n",
      "    to resolve C2H C2H2 C2H3 C2H4 C2H5O2 C2H6 C3H2 C3H4XA C3H4XP C3H5O C3H5XA C3H5XT C3H8 C4H6 C4H7 C4H8X1 C5H10X1 CH2 CH2CHO CH2GSG CH2OH CH3 CH3CHO CH3O HCO HO2 IXC3H6CO IXC3H7 IXC3H7O2 IXC4H7 IXC4H8 IXC4H9 IXC4H9O2 NXC3H7 NXC3H7O2 TXC4H9 TXC4H9O2 XXC7H13 YXC7H14 \n",
      "##############################################################################\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     0.0003844      3.788\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [112] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "##############################################################################\n",
      "Refining grid in flame.\n",
      "    New points inserted after grid points 37 38 39 40 \n",
      "    to resolve CH3O IXC3H7 IXC4H9 NXC3H7O2 \n",
      "##############################################################################\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [116] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "no new points needed in flame\n",
      "\n",
      "*********** Solving on 100 point grid with energy equation enabled ***********\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     5.339e-06      6.451\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     2.703e-05      6.359\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     3.421e-05      6.272\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     7.697e-05      6.065\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     0.0002598      4.937\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     0.0009864       4.13\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps      0.005618      2.908\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [100] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "grid refinement disabled.\n",
      "\n",
      "******************** Solving with grid refinement enabled ********************\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [100] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "##############################################################################\n",
      "Refining grid in flame.\n",
      "    New points inserted after grid points 33 34 35 36 37 \n",
      "    to resolve AXC5H10 C2H C2H2 C2H3 C2H3CHO C2H3CO C2H4 C2H5 C2H5O2 C3H2 C3H3 C3H4XA C3H4XP C3H5O C3H5XA C3H5XT C3H6 C3H8 C4H10 C4H6 C4H7 C4H8X1 C6H11 C6H12X1 CH CH2 CH2CHO CH2CO CH2GSG CH2OH CH3 CH3CHO CH3CO CH3COCH2 CH3COCH3 CH3O CH3O2H CH3OH CH4 CXC8H17 H H2 HCCO HCNO HCO HNCO HO2 HOCHO IXC3H5CHO IXC3H5CO IXC3H6CO IXC3H7 IXC3H7O2 IXC4H10 IXC4H6OH IXC4H7 IXC4H7O IXC4H7OH IXC4H8 IXC4H9 IXC4H9O2 IXC5H9 IXC8H18 NEOXC5H11 NXC3H7 NXC3H7O2 O OH TXC4H9 TXC4H9O2 XXC7H13 YXC7H14 YXC7H15 \n",
      "##############################################################################\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     0.0001709      4.564\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [105] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "##############################################################################\n",
      "Refining grid in flame.\n",
      "    New points inserted after grid points 34 35 36 37 38 39 \n",
      "    to resolve AXC5H10 C2H C2H3 C2H4 C2H5 C3H2 C3H3 C3H5O C3H5XT C4H10 C4H6 C4H7 C4H8X1 C5H10X1 C6H12X1 CH2 CH2CCH2OH CH2CHO CH2CO CH2GSG CH2OH CH3 CH3CHO CH3CO CH3O CH3O2 CH4 HCCO HCO IC4H8O2HXT IXC3H5CHO IXC3H5CO IXC3H7 IXC3H7O2 IXC4H10 IXC4H6OH IXC4H7 IXC4H9 IXC5H9 IXC8H18 NXC3H7O2 TXC4H9 TXC4H9O2 YXC7H14 \n",
      "##############################################################################\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     0.0003844      4.311\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [111] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "##############################################################################\n",
      "Refining grid in flame.\n",
      "    New points inserted after grid points 36 37 38 39 \n",
      "    to resolve C2H5O2 IXC3H7 IXC4H9 IXC4H9O2 TXC4H9 TXC4H9O2 \n",
      "##############################################################################\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [115] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "##############################################################################\n",
      "Refining grid in flame.\n",
      "    New points inserted after grid points 45 46 \n",
      "    to resolve C3H5XA \n",
      "##############################################################################\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [117] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "no new points needed in flame\n"
     ]
    }
   ],
   "source": [
    "i = 1\n",
    "for i in range(len(Phi)):\n",
    "    temp_values = []\n",
    "    CO_values = []\n",
    "    NO_values = []\n",
    "    grid_points = []\n",
    "    heat_release = []\n",
    "    velocity_values = []\n",
    "    # Set up the Cantera flame configuration\n",
    "    #gas = ct.Solution('gri30.yaml')  # Replace 'your_mechanism.cti' with the actual mechanism file\n",
    "    gas = ct.Solution('Jerzembeck.yaml')\n",
    "    phi = Phi[i]\n",
    "    P = 1.0 * ct.one_atm\n",
    "    Tin = 300.0\n",
    "    width = 0.02  # Adjust the domain width as needed\n",
    "    initial_grid = np.linspace(0, width, 100)\n",
    "\n",
    "    # Create a stoichiometric CH4/Air premixed mixture\n",
    "    #gas.set_equivalence_ratio(phi, \"CH4\", {\"O2\": 1.0, \"N2\": 3.76})\n",
    "    gas.set_equivalence_ratio(phi, 'IXC8H18', 'O2:12.5, N2:47')\n",
    "    gas.TP = Tin, P\n",
    "    flame = ct.FreeFlame(gas, grid=initial_grid)\n",
    "    # Run the flame simulation\n",
    "    flame.solve(loglevel=1, auto=True)\n",
    "    temp_values.append(flame.T)\n",
    "    CO_values.append(flame.X[12])\n",
    "    NO_values.append(flame.X[104])\n",
    "    grid_points.append(flame.grid)\n",
    "    heat_release.append(flame.heat_release_rate)\n",
    "    velocity_values.append(flame.velocity)\n",
    "    \n",
    "    final_temp_values.append(temp_values[0])\n",
    "    CO_Y_values.append(CO_values[0])\n",
    "    NO_Y_values.append(NO_values[0])\n",
    "    grid_values.append(grid_points[0])\n",
    "    max_heat_release_rate.append(heat_release[0])\n",
    "    flame_velocity_values.append(velocity_values[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "d26d2f26",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "115\n",
      "115\n",
      "115\n",
      "115\n",
      "[0.0245636  0.0245636  0.0245636  0.0245636  0.0245636  0.0245636\n",
      " 0.0245636  0.0245636  0.0245636  0.0245636  0.0245636  0.0245636\n",
      " 0.0245636  0.02456361 0.02456362 0.02456363 0.02456366 0.0245637\n",
      " 0.02456376 0.02456388 0.02456407 0.02456439 0.02456492 0.02456583\n",
      " 0.02456737 0.02457006 0.02457491 0.02458436 0.02460499 0.0246571\n",
      " 0.02480846 0.02529307 0.02690401 0.03196193 0.03783974 0.04233327\n",
      " 0.04808176 0.05147848 0.05522393 0.05930277 0.06369702 0.06838736\n",
      " 0.07334073 0.07850172 0.08378027 0.08903404 0.09405769 0.09860229\n",
      " 0.10245046 0.1055054  0.10781698 0.10953853 0.11085218 0.11272724\n",
      " 0.11416981 0.11634878 0.1179793  0.11922868 0.12092781 0.12210136\n",
      " 0.12296775 0.12363875 0.12417598 0.12461643 0.12498399 0.12529507\n",
      " 0.1255614  0.12579167 0.12599246 0.12616885 0.12632483 0.12646356\n",
      " 0.12658762 0.12669909 0.12679967 0.12689079 0.12697364 0.12704922\n",
      " 0.12711838 0.12718184 0.12724021 0.12729405 0.1273438  0.12738988\n",
      " 0.12743264 0.12747239 0.1275094  0.12754392 0.12757616 0.12760631\n",
      " 0.12763455 0.12766103 0.12768588 0.12770924 0.12773121 0.1277519\n",
      " 0.12777139 0.12778978 0.12780714 0.12782354 0.12783904 0.12785371\n",
      " 0.1278676  0.12788077 0.12789325 0.12790509 0.12791635 0.12792704\n",
      " 0.12793722 0.12794692 0.12795616 0.12796495 0.1279733  0.12798118\n",
      " 0.12798848 0.12799499 0.12800031 0.12800367 0.12800367]\n"
     ]
    }
   ],
   "source": [
    "print(len(grid_values[0]))\n",
    "print(len(final_temp_values[0]))\n",
    "print(len(CO_Y_values[0]))\n",
    "print(len(NO_Y_values[0]))\n",
    "print(flame.thermal_conductivity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "889e2532",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "48\n",
      "48\n",
      "49\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'\\ni = 1\\nfor i in range(max_heat_release_idx_06,len(time_scale_06)):\\n    actual_time_scale_06[max_heat_release_idx_06-1] =  0\\n    actual_time_scale_06[i] =  time_scale[i] + actual_time_scale[i-1]\\nfor i in range(39,-1,-1):\\n    actual_time_scale[40] =  0\\n    actual_time_scale[i] =  actual_time_scale[i+1] - time_scale[i]\\n'"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "time_scale_05 = np.zeros(len(grid_values[0]))\n",
    "time_scale_65 = np.zeros(len(grid_values[1]))\n",
    "time_scale_7 = np.zeros(len(grid_values[2]))\n",
    "#time_scale = []\n",
    "# Calculate the time scale\n",
    "def calculate_time_scale(grid, velocity): \n",
    "    for j in range(1,len(grid_values[0])-1):\n",
    "        delta_x = (grid[j+1]-grid[j-1])/2\n",
    "        vel = velocity[j]\n",
    "        time_scale[j] = delta_x/vel\n",
    "        #time_scale.append(delta_x/vel)\n",
    "    return time_scale\n",
    "\n",
    "time_scale_05 = calculate_time_scale(grid_values[0], flame_velocity_values[0])\n",
    "time_scale_05[0] = (grid_values[0][0]-grid_values[0][1])/flame_velocity_values[0][0]\n",
    "time_scale_05[-1] = (grid_values[0][-2]-grid_values[0][-1])/flame_velocity_values[0][-1]\n",
    "#length_flame = len(flame.grid)\n",
    "#print(np.diff(flame.grid))\n",
    "#print(len(time_scale))\n",
    "'''\n",
    "\n",
    "new_grid_06 = np.zeros(len(grid_values[0]))\n",
    "new_grid_65 = np.zeros(len(grid_values[1]))\n",
    "new_grid_7 = np.zeros(len(grid_values[2]))\n",
    "time_scale_06 = np.zeros(len(grid_values[0]))\n",
    "time_scale_65 = np.zeros(len(grid_values[1]))\n",
    "time_scale_7 = np.zeros(len(grid_values[2]))\n",
    "i = 1\n",
    "for i in range(1,len(grid_values[0])):\n",
    "    new_grid_06[i] = grid_values[0][i]- grid_values[0][i-1]\n",
    "    time_scale_06[i] = new_grid_06[i]/flame_velocity_values[0][i]\n",
    "    new_grid_65[i] = grid_values[1][i]- grid_values[1][i-1]\n",
    "    time_scale_65[i] = new_grid_65[i]/flame_velocity_values[1][i]\n",
    "    new_grid_7[i] = grid_values[2][i]- grid_values[2][i-1]\n",
    "    time_scale_7[i] = new_grid_7[i]/flame_velocity_values[2][i]\n",
    "\n",
    "\n",
    "# Find the position of maximum heat release\n",
    "max_heat_release_idx_06 = np.argmax(max_heat_release_rate[0])\n",
    "max_heat_release_idx_065 = np.argmax(max_heat_release_rate[1])\n",
    "max_heat_release_idx_7 = np.argmax(max_heat_release_rate[2])\n",
    "\n",
    "print(max_heat_release_idx_06)\n",
    "print(max_heat_release_idx_065)\n",
    "print(max_heat_release_idx_7)\n",
    "\n",
    "# to store the changed time scale t = 0 at maximum heat release\n",
    "actual_time_scale_06 = np.zeros(len(time_scale_06))\n",
    "actual_time_scale_65 = np.zeros(len(time_scale_65))\n",
    "actual_time_scale_7 = np.zeros(len(time_scale_7))\n",
    "\n",
    "def calculate_time_scale(time_scale, index):\n",
    "    time_scale_a = np.zeros(len(time_scale))\n",
    "    i = 1\n",
    "    for i in range(index,len(time_scale)):\n",
    "        time_scale_a[index-1] = 0\n",
    "        time_scale_a[i] = time_scale[i] + time_scale_a[i-1]\n",
    "    for i in range(index-2,-1,-1):\n",
    "        time_scale_a[index-1] = 0\n",
    "        time_scale_a[i] = time_scale_a[i+1] - time_scale[i] \n",
    "    return time_scale_a             \n",
    "\n",
    "actual_time_scale_06 = calculate_time_scale(time_scale_06,max_heat_release_idx_06)\n",
    "actual_time_scale_65 = calculate_time_scale(time_scale_65,max_heat_release_idx_065)\n",
    "actual_time_scale_7 = calculate_time_scale(time_scale_7,max_heat_release_idx_7)\n",
    "\n",
    "'''\n",
    "i = 1\n",
    "for i in range(max_heat_release_idx_06,len(time_scale_06)):\n",
    "    actual_time_scale_06[max_heat_release_idx_06-1] =  0\n",
    "    actual_time_scale_06[i] =  time_scale[i] + actual_time_scale[i-1]\n",
    "for i in range(39,-1,-1):\n",
    "    actual_time_scale[40] =  0\n",
    "    actual_time_scale[i] =  actual_time_scale[i+1] - time_scale[i]\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "93d0c99f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.01, 0.01)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a plot\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(actual_time_scale_06, final_temp_values[0], label=\"phi = 0.6\")\n",
    "plt.plot(actual_time_scale_65, final_temp_values[1], label=\"phi = 0.65\")\n",
    "plt.plot(actual_time_scale_7, final_temp_values[2], label=\"phi = 0.7\")\n",
    "\n",
    "\n",
    "plt.xlabel(\"Time (s)\")\n",
    "plt.ylabel(\"Temperature (K)\")\n",
    "plt.title('Temperature (K) vs. Time')\n",
    "plt.legend(loc='upper right', bbox_to_anchor=(1.3, 1))\n",
    "plt.xlim([-0.01, 0.01])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "b8dfebcb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.008, 0.0075)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a plot\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(actual_time_scale_06, CO_Y_values[0], label=\"phi = 0.6\")\n",
    "plt.plot(actual_time_scale_65, CO_Y_values[1], label=\"phi = 0.65\")\n",
    "plt.plot(actual_time_scale_7, CO_Y_values[2], label=\"phi = 0.7\")\n",
    "\n",
    "\n",
    "plt.xlabel(\"Time (s)\")\n",
    "plt.ylabel(\"Mass fraction_CO\")\n",
    "plt.title('Mass fraction_CO vs. Time (s)')\n",
    "plt.legend(loc='upper right', bbox_to_anchor=(1.3, 1))\n",
    "plt.xlim([-0.008, 0.0075])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "3c9d5207",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.008, 0.01)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a plot\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(actual_time_scale_06, NO_Y_values[0], label=\"phi = 0.6\")\n",
    "plt.plot(actual_time_scale_65, NO_Y_values[1], label=\"phi = 0.65\")\n",
    "plt.plot(actual_time_scale_7, NO_Y_values[2], label=\"phi = 0.7\")\n",
    "\n",
    "\n",
    "plt.xlabel(\"Time (s)\")\n",
    "plt.ylabel(\"Mass fraction_NO\")\n",
    "plt.title('Mass fraction_NO vs. Time (s)')\n",
    "plt.legend(loc='upper right', bbox_to_anchor=(1.3, 1))\n",
    "plt.xlim([-0.008, 0.01])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1a037b9",
   "metadata": {},
   "source": [
    "Question- 9, varying phi from 0.5 to 1.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a820338a",
   "metadata": {},
   "outputs": [],
   "source": [
    "Phi = np.arange(0.5,1.5,0.1)\n",
    "velocity_flame_Q9 = []\n",
    "velocity_values_Q9 = []\n",
    "heat_release_values_Q9 = []\n",
    "print(Phi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "07e740e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "j = 1\n",
    "for k in range(len(Phi)):\n",
    "    velocity_points = []\n",
    "    heat_release_points = []\n",
    "    \n",
    "    j = j+1\n",
    "    # Set up the Cantera flame configuration\n",
    "    #gas = ct.Solution('gri30.yaml')  # Replace 'your_mechanism.cti' with the actual mechanism file\n",
    "    gas = ct.Solution('Jerzembeck.yaml')\n",
    "    phi = Phi[k]\n",
    "    P = 1.0 * ct.one_atm\n",
    "    Tin = 300.0\n",
    "    width = 0.02  # Adjust the domain width as needed\n",
    "    initial_grid = np.linspace(0, width, 100)\n",
    "\n",
    "    # Create a stoichiometric CH4/Air premixed mixture\n",
    "    #gas.set_equivalence_ratio(phi, \"CH4\", {\"O2\": 1.0, \"N2\": 3.76})\n",
    "    gas.set_equivalence_ratio(phi, 'IXC8H18', 'O2:12.5, N2:47')\n",
    "    gas.TP = Tin, P\n",
    "    flame = ct.FreeFlame(gas, grid=initial_grid)\n",
    "    # Run the flame simulation\n",
    "    flame.solve(loglevel=1, auto=True)\n",
    "    \n",
    "    velocity_points.append(flame.velocity)\n",
    "    heat_release_points.append(flame.heat_release_rate)\n",
    "    Su0 = flame.velocity[0]\n",
    "    \n",
    "    print(f\"Flame Speed is: {Su0 * 100:.2f} cm/s\")\n",
    "    velocity_flame_Q9.append(Su0)\n",
    "    velocity_values_Q9.append(velocity_points[0])\n",
    "    heat_release_values_Q9.append(heat_release_points[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "080655e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.059043949789809445, 0.13819797037757764, 0.21789358895757258, 0.29743369582734147, 0.34754241667144903, 0.37903488000144164, 0.383351100840101, 0.33372649370609314, 0.26909312937140045, 0.1664432315319703]\n",
      "[0.5 0.6 0.7 0.8 0.9 1.  1.1 1.2 1.3 1.4]\n"
     ]
    }
   ],
   "source": [
    "print(velocity_flame_Q9)\n",
    "print(Phi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "bd3ccbe5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(Phi, velocity_flame_Q9,\"-o\")\n",
    "plt.xlabel(\"Equivalence ratio (phi)\")\n",
    "plt.ylabel(\"Laminar flame speed (m/s)\");\n",
    "plt.title('Laminar flame speed vs. Equivalence ratio')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e99cc85a",
   "metadata": {},
   "source": [
    "Question- 10, varying initial pressure from 1 to 16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9d793abb",
   "metadata": {},
   "outputs": [],
   "source": [
    "P_initial = [1,2,4,8,16]\n",
    "velocity_flame_Q10 = []\n",
    "density_flame_values = []\n",
    "thermal_conductivity_values = []\n",
    "specific_heat_values = []\n",
    "velocity_values_Q10 = []\n",
    "heat_release_values_Q10 = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d3fe11a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "*********** Solving on 100 point grid with energy equation enabled ***********\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     8.438e-06      6.707\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     1.424e-05      6.465\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     1.802e-05      6.293\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     4.055e-05      6.074\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     0.0002053      4.906\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [100] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "grid refinement disabled.\n",
      "\n",
      "******************** Solving with grid refinement enabled ********************\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [100] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "##############################################################################\n",
      "Refining grid in flame.\n",
      "    New points inserted after grid points 32 33 34 35 36 \n",
      "    to resolve AXC5H10 C2H C2H2 C2H3 C2H3CHO C2H4 C2H5 C2H5O2 C2H6 C3H2 C3H3 C3H4XA C3H4XP C3H5XA C3H5XT C3H6 C3H8 C4H6 C4H7 C4H8X1 C5H10X1 CH CH2 CH2CCH2OH CH2CHO CH2CO CH2GSG CH2O CH2OH CH3 CH3CHO CH3COCH2 CH3COCH3 CH3O CH3O2 CH3OH CH4 CXC8H17 H2 H2O H2O2 HCCO HCN HCNO HCO HO2 HOCHO IXC3H5CHO IXC3H6CO IXC3H7 IXC3H7O2 IXC4H10 IXC4H6OH IXC4H7 IXC4H7OH IXC4H8 IXC4H9 IXC4H9O2 IXC5H9 IXC8H18 N2 NEOXC5H11 NXC3H7 NXC3H7O2 O OH TXC4H9 TXC4H9O2 YXC7H14 YXC7H15 \n",
      "##############################################################################\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     0.0001709      4.548\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [105] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "##############################################################################\n",
      "Refining grid in flame.\n",
      "    New points inserted after grid points 34 35 36 37 38 39 \n",
      "    to resolve AXC5H10 C2H C2H2 C2H3 C2H3CHO C2H4 C2H5 C3H2 C3H4XA C3H4XP C3H5XA C3H5XT C3H6 C4H10 C4H6 C4H7 CH CH2 CH2CHO CH2CO CH2GSG CH2OH CH3 CH3CHO CH3O CH3OH CH4 HCN HCO HO2 IXC3H5CHO IXC3H6CO IXC3H7 IXC3H7O2 IXC4H10 IXC4H7 IXC4H9 IXC4H9O2 IXC5H9 IXC8H18 NXC3H7 NXC3H7O2 TXC4H9 TXC4H9O2 \n",
      "##############################################################################\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [111] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "##############################################################################\n",
      "Refining grid in flame.\n",
      "    New points inserted after grid points 36 37 38 40 41 \n",
      "    to resolve CH3O IXC5H9 NXC3H7O2 \n",
      "##############################################################################\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [116] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "no new points needed in flame\n",
      "Flame Speed is: 63.13 cm/s\n",
      "\n",
      "*********** Solving on 100 point grid with energy equation enabled ***********\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     4.219e-06      6.964\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     8.009e-06      6.722\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     2.027e-05      6.619\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     1.283e-05      6.908\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     1.624e-05      6.883\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps      1.37e-05      6.499\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     3.468e-05      6.051\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     2.194e-05      5.889\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     0.0003749      4.811\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     2.966e-05      5.729\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [100] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "grid refinement disabled.\n",
      "\n",
      "******************** Solving with grid refinement enabled ********************\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [100] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "##############################################################################\n",
      "Refining grid in flame.\n",
      "    New points inserted after grid points 33 34 35 36 \n",
      "    to resolve AXC5H10 C2H C2H2 C2H3 C2H3CHO C2H3CO C2H5 C2H5O2 C2H6 C3H2 C3H3 C3H4XA C3H4XP C3H5O C3H5XA C3H5XT C3H6 C3H8 C4H10 C4H6 C4H7 C4H8X1 C5H10X1 C5H9 C6H11 C6H12X1 CH CH2 CH2CCH2OH CH2CHO CH2CO CH2GSG CH2O CH2OH CH3 CH3CHO CH3CO CH3COCH2 CH3COCH3 CH3O CH3O2 CH3O2H CH3OH CH4 CO CXC8H17 H2O H2O2 HCCO HCN HCNO HCO HNCO HO2 HOCHO IC4H8O2HXT IXC3H5CHO IXC3H5CO IXC3H6CO IXC3H7 IXC3H7O2 IXC4H10 IXC4H6OH IXC4H7 IXC4H7O IXC4H7OH IXC4H8 IXC4H9 IXC4H9O2 IXC5H9 IXC8H18 N2 NEOXC5H11 NO2 NXC3H7 NXC3H7O2 O OH TXC4H9 TXC4H9O2 XXC7H13 YXC7H14 YXC7H15 \n",
      "##############################################################################\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     0.0001709      4.672\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [104] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "##############################################################################\n",
      "Refining grid in flame.\n",
      "    New points inserted after grid points 34 35 36 37 \n",
      "    to resolve AXC5H10 C2H C2H3 C2H3CHO C2H4 C2H5 C2H5O2 C2H6 C3H2 C3H3 C3H4XA C3H4XP C3H5XA C3H5XT C3H6 C3H8 C4H10 C4H6 C4H7 C4H8X1 C5H10X1 C6H12X1 CH CH2 CH2CCH2OH CH2CHO CH2CO CH2GSG CH2O CH2OH CH3 CH3CHO CH3COCH3 CH3O CH3O2 CH3O2H CH3OH CH4 CO CXC8H17 H2 H2O H2O2 HCCO HCN HCO HNCO HO2 IC4H8O2HXT IXC3H5CHO IXC3H6CO IXC3H7 IXC3H7O2 IXC4H10 IXC4H6OH IXC4H7 IXC4H7O IXC4H7OH IXC4H8 IXC4H9 IXC4H9O2 IXC5H9 NEOXC5H11 NO2 NXC3H7 NXC3H7O2 OH TXC4H9 TXC4H9O2 YXC7H14 YXC7H15 \n",
      "##############################################################################\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [108] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "##############################################################################\n",
      "Refining grid in flame.\n",
      "    New points inserted after grid points 35 36 37 38 39 40 \n",
      "    to resolve AXC5H10 C2H2 C2H3 C2H5 C3H2 C3H3 C3H4XA C3H4XP C3H5XA C3H5XT C3H6 C4H7 C4H8X1 C5H10X1 CH CH2 CH2CCH2OH CH2GSG CH2OH CH3CO CH3O CH3O2 HCCO HCNO HCO HO2 HOCHO IXC3H5CHO IXC3H7O2 IXC4H6OH IXC4H9 IXC5H9 NXC3H7O2 TXC4H9 YXC7H14 YXC7H15 \n",
      "##############################################################################\n",
      "\n",
      "..............................................................................\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [114] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "no new points needed in flame\n",
      "Flame Speed is: 57.12 cm/s\n",
      "\n",
      "*********** Solving on 100 point grid with energy equation enabled ***********\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     2.109e-06      7.214\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     1.068e-05      6.736\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     1.014e-05      6.451\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     9.622e-06      6.476\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     3.425e-06      7.167\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     3.901e-05      6.044\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     1.389e-05      6.462\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     6.591e-06      7.157\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     5.005e-05      5.888\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     6.681e-06      7.127\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     5.073e-05      5.573\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     2.408e-05      6.383\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     6.856e-05      5.209\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps      0.001757      3.719\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [100] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "grid refinement disabled.\n",
      "\n",
      "******************** Solving with grid refinement enabled ********************\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [100] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "##############################################################################\n",
      "Refining grid in flame.\n",
      "    New points inserted after grid points 33 34 35 \n",
      "    to resolve AXC5H10 C2H C2H2 C2H3 C2H3CHO C2H3CO C2H4 C2H5 C2H5O2 C2H6 C3H2 C3H3 C3H4XA C3H4XP C3H5O C3H5XA C3H5XT C3H6 C3H8 C4H10 C4H6 C4H8X1 C5H10X1 C6H11 C6H12X1 CH CH2 CH2CCH2OH CH2CO CH2GSG CH2O CH2OH CH3 CH3CHO CH3COCH2 CH3COCH3 CH3O CH3O2 CH3O2H CH3OH CH4 CO CXC8H17 H H2O H2O2 HCCO HCN HCNO HCO HNCO HO2 HOCHO IC4H8O2HXT IXC3H5CHO IXC3H5CO IXC3H6CO IXC3H7 IXC3H7O2 IXC4H10 IXC4H6OH IXC4H7 IXC4H7O IXC4H7OH IXC4H8 IXC4H9 IXC4H9O2 IXC5H9 IXC8H18 N2 NEOXC5H11 NO NO2 NXC3H7 NXC3H7O2 O O2 OH T TXC4H9 TXC4H9O2 XXC7H13 YXC7H14 YXC7H15 velocity \n",
      "##############################################################################\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     0.0001709      4.548\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [103] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "##############################################################################\n",
      "Refining grid in flame.\n",
      "    New points inserted after grid points 34 35 36 37 \n",
      "    to resolve AXC5H10 C2H C2H2 C2H3 C2H3CHO C2H4 C2H5 C2H5O2 C2H6 C3H2 C3H3 C3H4XA C3H4XP C3H5O C3H5XA C3H5XT C3H6 C3H8 C4H10 C4H6 C4H7 C4H8X1 C5H10X1 C6H12X1 CH CH2 CH2CCH2OH CH2CHO CH2CO CH2GSG CH2O CH2OH CH3 CH3CHO CH3CO CH3COCH3 CH3O CH3O2 CH3O2H CH3OH CH4 CXC8H17 H H2 HCCO HCNO HCO HNCO HO2 HOCHO IXC3H5CHO IXC3H5CO IXC3H6CO IXC3H7 IXC3H7O2 IXC4H10 IXC4H6OH IXC4H7 IXC4H7O IXC4H7OH IXC4H8 IXC4H9 IXC4H9O2 IXC5H9 IXC8H18 N2 NEOXC5H11 NO2 NXC3H7 NXC3H7O2 O O2 OH TXC4H9 TXC4H9O2 XXC7H13 YXC7H14 YXC7H15 \n",
      "##############################################################################\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     7.594e-05      5.384\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [107] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "##############################################################################\n",
      "Refining grid in flame.\n",
      "    New points inserted after grid points 35 36 37 38 39 \n",
      "    to resolve AXC5H10 C2H C2H2 C2H3 C2H3CHO C2H5 C2H5O2 C2H6 C3H2 C3H3 C3H4XA C3H4XP C3H5XA C3H5XT C3H6 C3H8 C4H10 C4H7 C4H8X1 C5H10X1 C6H12X1 CH2 CH2CCH2OH CH2CHO CH2CO CH2GSG CH2O CH2OH CH3 CH3O CH3O2 CH3O2H CH3OH CH4 CO CXC8H17 H HCCO HCN HCNO HCO HNCO HO2 HOCHO IC4H8O2HXT IXC3H5CHO IXC3H6CO IXC3H7 IXC3H7O2 IXC4H10 IXC4H6OH IXC4H7 IXC4H7OH IXC4H8 IXC4H9 IXC4H9O2 IXC5H9 NEOXC5H11 NO2 NXC3H7 NXC3H7O2 TXC4H9 TXC4H9O2 XXC7H13 YXC7H14 YXC7H15 \n",
      "##############################################################################\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [112] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "##############################################################################\n",
      "Refining grid in flame.\n",
      "    New points inserted after grid points 36 37 38 39 40 41 \n",
      "    to resolve C2H C2H3 C2H4 C2H6 C3H2 C3H4XA C3H4XP C3H5XA C3H5XT C4H6 C5H10X1 CH2 CH2CCH2OH CH2CHO CH2GSG CH2OH CH3 CH3CHO CH3OH HCCO HCO IXC3H5CO IXC3H6CO IXC3H7O2 IXC4H7 IXC4H8 IXC4H9O2 NXC3H7O2 YXC7H14 \n",
      "##############################################################################\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     0.0003844      4.401\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [118] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "no new points needed in flame\n",
      "Flame Speed is: 48.20 cm/s\n",
      "\n",
      "*********** Solving on 100 point grid with energy equation enabled ***********\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     1.582e-06      7.259\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     6.007e-06      6.967\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     2.851e-06      7.365\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     8.118e-06      6.678\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     3.082e-05      6.033\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     9.753e-06      6.446\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     6.943e-06      6.651\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     2.636e-05      5.735\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     8.341e-06      6.845\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     1.188e-05      6.239\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     5.073e-05      5.939\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     4.514e-06      6.977\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     7.713e-05      5.517\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     6.864e-06      6.786\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     3.909e-05      5.381\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps      0.001002      3.981\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [100] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "grid refinement disabled.\n",
      "\n",
      "******************** Solving with grid refinement enabled ********************\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [100] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "##############################################################################\n",
      "Refining grid in flame.\n",
      "    New points inserted after grid points 33 34 35 \n",
      "    to resolve AXC5H10 C2H C2H2 C2H3 C2H3CHO C2H4 C2H5O2 C2H6 C3H2 C3H3 C3H4XA C3H4XP C3H5XA C3H5XT C3H6 C3H8 C4H10 C4H8X1 CH2 CH2CCH2OH CH2CO CH2O CH2OH CH3 CH3CHO CH3COCH2 CH3COCH3 CH3O CH3O2 CH3O2H CH3OH CH4 CO CO2 CXC8H17 H H2O H2O2 HCCO HCN HCNO HCO HNCO HO2 HOCHO IC4H8O2HXT IXC3H5CHO IXC3H5CO IXC3H6CO IXC3H7 IXC3H7O2 IXC4H10 IXC4H6OH IXC4H7 IXC4H7O IXC4H7OH IXC4H8 IXC4H9 IXC4H9O2 IXC5H9 IXC8H18 N2 NEOXC5H11 NO NO2 NXC3H7O2 O O2 OH T TXC4H9 TXC4H9O2 XXC7H13 YXC7H14 YXC7H15 velocity \n",
      "##############################################################################\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     2.848e-05      6.113\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     3.041e-05       5.73\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [103] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "##############################################################################\n",
      "Refining grid in flame.\n",
      "    New points inserted after grid points 34 35 36 \n",
      "    to resolve AXC5H10 C2H C2H2 C2H3 C2H3CHO C2H3CO C2H4 C2H5 C2H5O2 C2H6 C3H2 C3H3 C3H4XA C3H4XP C3H5O C3H5XA C3H5XT C3H6 C3H8 C4H10 C4H6 C4H8X1 CH2 CH2CCH2OH CH2CO CH2GSG CH2O CH2OH CH3 CH3CHO CH3COCH2 CH3COCH3 CH3O CH3O2 CH3O2H CH3OH CH4 CO CXC8H17 H H2O H2O2 HCCO HCN HCNO HCO HNCO HO2 HOCHO IC4H8O2HXT IXC3H5CHO IXC3H5CO IXC3H6CO IXC3H7 IXC3H7O2 IXC4H10 IXC4H6OH IXC4H7 IXC4H7O IXC4H7OH IXC4H8 IXC4H9 IXC4H9O2 IXC5H9 IXC8H18 N2 NEOXC5H11 NO NO2 NXC3H7 NXC3H7O2 O O2 OH T TXC4H9 TXC4H9O2 XXC7H13 YXC7H14 YXC7H15 velocity \n",
      "##############################################################################\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     0.0001709      4.875\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [106] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "##############################################################################\n",
      "Refining grid in flame.\n",
      "    New points inserted after grid points 35 36 37 38 \n",
      "    to resolve AXC5H10 C2H C2H2 C2H3 C2H3CHO C2H4 C2H5 C2H5O2 C2H6 C3H2 C3H3 C3H4XA C3H4XP C3H5O C3H5XA C3H5XT C3H6 C4H10 C4H6 C4H7 C4H8X1 C5H10X1 C6H12X1 CH2 CH2CCH2OH CH2CHO CH2CO CH2GSG CH2O CH2OH CH3 CH3CHO CH3COCH2 CH3COCH3 CH3O CH3O2 CH3O2H CH3OH CH4 CXC8H17 H H2 H2O H2O2 HCCO HCN HCO HO2 HOCHO IC4H8O2HXT IXC3H5CHO IXC3H5CO IXC3H6CO IXC3H7 IXC3H7O2 IXC4H10 IXC4H6OH IXC4H7 IXC4H7O IXC4H7OH IXC4H8 IXC4H9 IXC4H9O2 IXC5H9 IXC8H18 N2 NEOXC5H11 NO2 NXC3H7 NXC3H7O2 O O2 OH TXC4H9 TXC4H9O2 YXC7H14 YXC7H15 velocity \n",
      "##############################################################################\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     7.594e-05      5.175\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [110] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "##############################################################################\n",
      "Refining grid in flame.\n",
      "    New points inserted after grid points 36 37 38 39 40 \n",
      "    to resolve AXC5H10 C2H C2H2 C2H3 C2H3CHO C2H4 C2H5 C2H6 C3H2 C3H3 C3H4XA C3H4XP C3H5XA C3H5XT C3H6 C3H8 C4H10 C4H6 C4H7 C4H8X1 C5H10X1 CH CH2 CH2CCH2OH CH2CHO CH2CO CH2GSG CH2OH CH3 CH3CHO CH3COCH3 CH3O CH3O2 CH3O2H CH3OH CH4 H H2 HCCO HCN HCO HO2 HOCHO IXC3H5CHO IXC3H5CO IXC3H6CO IXC3H7 IXC3H7O2 IXC4H10 IXC4H6OH IXC4H7 IXC4H7OH IXC4H9 IXC4H9O2 IXC5H9 IXC8H18 NXC3H7 NXC3H7O2 OH TXC4H9 TXC4H9O2 XXC7H13 YXC7H15 \n",
      "##############################################################################\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     0.0002563       4.53\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [115] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "##############################################################################\n",
      "Refining grid in flame.\n",
      "    New points inserted after grid points 37 38 39 40 41 42 43 \n",
      "    to resolve AXC5H10 C2H C2H2 C3H2 C3H4XA C3H4XP C3H5XA C3H5XT C4H7 C4H8X1 C5H10X1 CH2 CH2CHO CH2GSG CH2OH CH3O HO2 IXC3H5CO IXC3H6CO IXC4H7 IXC4H9O2 IXC5H9 NXC3H7O2 TXC4H9O2 XXC7H13 YXC7H14 YXC7H15 \n",
      "##############################################################################\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     0.0003844      4.313\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [122] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "no new points needed in flame\n",
      "Flame Speed is: 39.42 cm/s\n",
      "\n",
      "*********** Solving on 100 point grid with energy equation enabled ***********\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     1.187e-06      7.527\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps      2.67e-06      7.189\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     4.505e-06       7.04\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     8.553e-06      6.743\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     4.567e-06      7.284\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     2.601e-05      5.613\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     4.629e-06      7.156\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     7.909e-05      5.631\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     2.346e-06      7.242\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     4.008e-05      5.505\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     7.134e-06      7.266\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     4.063e-05      5.445\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     5.785e-05      6.194\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     5.148e-06      6.917\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     0.0001319      4.963\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps      0.003381      3.539\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [100] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "grid refinement disabled.\n",
      "\n",
      "******************** Solving with grid refinement enabled ********************\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [100] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "##############################################################################\n",
      "Refining grid in flame.\n",
      "    New points inserted after grid points 33 34 35 \n",
      "    to resolve AXC5H10 C2H2 C2H3CHO C2H4 C2H6 C3H2 C3H3 C3H4XA C3H4XP C3H5XA C3H6 C3H8 C4H8X1 CH2 CH2CO CH2O CH2OH CH3 CH3CHO CH3COCH3 CH3O CH3O2 CH3O2H CH3OH CH4 CO CO2 CXC8H17 H H2 H2O H2O2 HCCO HCN HCNO HCO HNCO HO2 HOCHO IXC3H5CHO IXC3H6CO IXC3H7 IXC3H7O2 IXC4H10 IXC4H6OH IXC4H7 IXC4H7OH IXC4H8 IXC4H9 IXC4H9O2 IXC8H18 N2 N2O NCO NEOXC5H11 NO NO2 NXC3H7O2 O O2 OH T TXC4H9 TXC4H9O2 XXC7H13 YXC7H14 velocity \n",
      "##############################################################################\n",
      "\n",
      "..............................................................................\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     5.625e-06      6.633\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [103] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "##############################################################################\n",
      "Refining grid in flame.\n",
      "    New points inserted after grid points 34 35 36 \n",
      "    to resolve AXC5H10 C2H C2H2 C2H3 C2H3CHO C2H4 C2H5O2 C2H6 C3H2 C3H3 C3H4XA C3H4XP C3H5XA C3H5XT C3H6 C3H8 C4H8X1 CH2 CH2CCH2OH CH2CO CH2O CH2OH CH3 CH3CHO CH3COCH2 CH3COCH3 CH3O CH3O2 CH3O2H CH3OH CH4 CO CO2 CXC8H17 H H2O H2O2 HCCO HCN HCNO HCO HNCO HO2 HOCHO IC4H8O2HXT IXC3H5CHO IXC3H5CO IXC3H6CO IXC3H7 IXC3H7O2 IXC4H10 IXC4H6OH IXC4H7 IXC4H7O IXC4H7OH IXC4H8 IXC4H9 IXC4H9O2 IXC5H9 IXC8H18 N2 N2O NCO NEOXC5H11 NO NO2 NXC3H7O2 O O2 OH T TXC4H9 TXC4H9O2 XXC7H13 YXC7H14 velocity \n",
      "##############################################################################\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     0.0001139      5.006\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     1.014e-05      6.152\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     7.697e-05      5.056\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [106] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "##############################################################################\n",
      "Refining grid in flame.\n",
      "    New points inserted after grid points 35 36 37 \n",
      "    to resolve AXC5H10 C2H C2H2 C2H3 C2H3CHO C2H4 C2H5 C2H5O2 C2H6 C3H2 C3H3 C3H4XA C3H4XP C3H5O C3H5XA C3H5XT C3H6 C3H8 C4H6 CH2 CH2CCH2OH CH2CHO CH2CO CH2GSG CH2O CH2OH CH3 CH3CHO CH3COCH2 CH3COCH3 CH3O CH3O2 CH3O2H CH3OH CH4 CO CXC8H17 H H2 H2O H2O2 HCCO HCN HCNO HCO HNCO HO2 HOCHO IC4H8O2HXT IXC3H5CHO IXC3H5CO IXC3H6CO IXC3H7 IXC3H7O2 IXC4H10 IXC4H6OH IXC4H7 IXC4H7O IXC4H7OH IXC4H8 IXC4H9 IXC4H9O2 IXC5H9 IXC8H18 N2 NEOXC5H11 NO NO2 NXC3H7O2 O O2 T TXC4H9 TXC4H9O2 XXC7H13 YXC7H14 YXC7H15 velocity \n",
      "##############################################################################\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     8.543e-05      5.427\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [109] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "##############################################################################\n",
      "Refining grid in flame.\n",
      "    New points inserted after grid points 36 37 38 39 \n",
      "    to resolve AXC5H10 C2H C2H2 C2H3 C2H3CHO C2H4 C2H5 C2H5O2 C2H6 C3H2 C3H3 C3H4XA C3H4XP C3H5O C3H5XA C3H5XT C3H6 C3H8 C4H10 C4H6 C4H7 C4H8X1 C5H10X1 C6H12X1 CH2 CH2CCH2OH CH2CO CH2GSG CH2O CH2OH CH3 CH3CHO CH3COCH2 CH3COCH3 CH3O CH3O2 CH3O2H CH3OH CH4 CO CXC8H17 H H2 H2O H2O2 HCCO HCN HCNO HCO HNCO HOCHO IC4H8O2HXT IXC3H5CHO IXC3H5CO IXC3H6CO IXC3H7 IXC3H7O2 IXC4H10 IXC4H6OH IXC4H7 IXC4H7O IXC4H7OH IXC4H8 IXC4H9 IXC4H9O2 IXC5H9 N2 NEOXC5H11 NXC3H7 NXC3H7O2 O OH TXC4H9 TXC4H9O2 XXC7H13 YXC7H14 YXC7H15 \n",
      "##############################################################################\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     0.0001709      5.006\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [113] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "##############################################################################\n",
      "Refining grid in flame.\n",
      "    New points inserted after grid points 37 38 39 40 41 42 \n",
      "    to resolve AXC5H10 C2H2 C2H3 C2H3CO C2H4 C2H5 C2H5O2 C2H6 C3H3 C3H4XA C3H4XP C3H5XA C3H5XT C3H6 C4H10 C4H6 C4H7 C4H8X1 C5H10X1 C6H12X1 CH2 CH2CCH2OH CH2CHO CH2OH CH3 CH3COCH2 CH3O CH3OH H HCCO HCO HO2 IXC3H5CO IXC3H7 IXC3H7O2 IXC4H10 IXC4H6OH IXC4H7 IXC4H7O IXC4H8 IXC4H9O2 IXC5H9 NXC3H7 TXC4H9 TXC4H9O2 XXC7H13 YXC7H14 YXC7H15 \n",
      "##############################################################################\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     0.0002563       4.51\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [119] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "##############################################################################\n",
      "Refining grid in flame.\n",
      "    New points inserted after grid points 42 43 44 45 \n",
      "    to resolve C3H3 CH2CCH2OH CH2OH HCCO IXC4H7 XXC7H13 YXC7H15 \n",
      "##############################################################################\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [123] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "no new points needed in flame\n",
      "Flame Speed is: 30.17 cm/s\n"
     ]
    }
   ],
   "source": [
    "j = 1\n",
    "for l in range(len(P_initial)):\n",
    "    density_points = []\n",
    "    thermal_conductivity_points = []\n",
    "    specific_heat_points = []\n",
    "    velocity_points = []\n",
    "    heat_release_points = []\n",
    "    \n",
    "    j = j+1\n",
    "    # Set up the Cantera flame configuration\n",
    "    #gas = ct.Solution('gri30.yaml')  # Replace 'your_mechanism.cti' with the actual mechanism file\n",
    "    gas = ct.Solution('Jerzembeck.yaml')\n",
    "    phi = 0.6\n",
    "    P = P_initial[l] * ct.one_atm\n",
    "    Tin = 600\n",
    "    width = 0.02  # Adjust the domain width as needed\n",
    "    initial_grid = np.linspace(0, width, 100)\n",
    "\n",
    "    # Create a stoichiometric CH4/Air premixed mixture\n",
    "    #gas.set_equivalence_ratio(phi, \"CH4\", {\"O2\": 1.0, \"N2\": 3.76})\n",
    "    gas.set_equivalence_ratio(phi, 'IXC8H18', 'O2:12.5, N2:47')\n",
    "    gas.TP = Tin, P\n",
    "    flame = ct.FreeFlame(gas, grid=initial_grid)\n",
    "    # Run the flame simulation\n",
    "    flame.solve(loglevel=1, auto=True)\n",
    "    density_points.append(flame.density_mass)\n",
    "    thermal_conductivity_points.append(flame.thermal_conductivity)\n",
    "    specific_heat_points.append(flame.cp_mass)\n",
    "    velocity_points.append(flame.velocity)\n",
    "    heat_release_points.append(flame.heat_release_rate)\n",
    "    \n",
    "    Su0 = flame.velocity[0]\n",
    "    \n",
    "    print(f\"Flame Speed is: {Su0 * 100:.2f} cm/s\")\n",
    "    velocity_flame_Q10.append(Su0)\n",
    "    density_flame_values.append(density_points[0])\n",
    "    thermal_conductivity_values.append(thermal_conductivity_points[0])\n",
    "    specific_heat_values.append(specific_heat_points[0])\n",
    "    velocity_values_Q10.append(velocity_points[0])\n",
    "    heat_release_values_Q10.append(heat_release_points[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "03e3d808",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WQFQjbNu2DQ0aNMD27dvVftk8+cu9Jrh06RJu3LiBdevWYfjw4ar28gzzVJXdu3ejsLAQu3btUuvdqeru8wYNGgAA9PX1y/UBVRYDAwMEBwcjODgYSqUS7777LlauXIkZM2aoFSI3b97Eiy++qHqem5uLpKQkvPzyywCAhg0bAnhcVD0riyaZ69Wrp+pZ+reYmJhyn19ISAjeffddxMTEYPPmzTAxMUFwcHCp7by8vODl5YX//e9/OHnyJDp06IAVK1bg008/Lfd7aYMmK4xv27YNL774In744Qe19szMTNUE6urUsGFDCIIADw8PVQ/KszzveyCVStG9e3d0794dX3/9NebNm4ePP/4YR44cQWBgoKo3+L+LRpZ1JeCzMl+4cAHdu3fn6u41BIfAqEZ48lfcv3tqzpw5g1OnTokVqZSyMgqCoHZptxgZsrKysGbNmip9HwcHB3Tt2hUrV65EUlJSqddTU1OfuX96errac6lUilatWgFAqUt+V61aheLiYtXz5cuXo6SkBL169QIABAUFwcLCAvPmzVPb7r9ZNMn88ssv4/Tp04iIiFB7/eeff37mef3bq6++CplMho0bN2Lr1q145ZVXYGpqqno9OzsbJSUlavt4eXlBKpWqfQ3i4+Nx/fr1cr9vVXmStTwrQctkslK9qFu3bi3X3DptGDBgAGQyGebMmVMqlyAIqp+/8nwP/rtMBgDVVadPtnlShP/555+qbRQKBVatWlXuzIMGDcK9e/ewevXqUq89evSoVq5vVNuxB4iqzY8//ljmPJAJEybglVdewfbt29G/f3/07t0bsbGxWLFiBTw9PZGbmytC2tKaNWuGhg0bYvLkybh37x4sLCzw66+/Vulci+fp0aOHqmdl9OjRyM3NxerVq+Hg4FDmh35lLFu2DB07doSXlxfeeustNGjQAA8ePMCpU6eQmJj4zDVg3nzzTWRkZKBbt25wc3NDXFwclixZgtatW6N58+Zq2xYVFaF79+6qy/C/++47dOzYEX369AEAWFhYYPny5Rg2bBjatGmD0NBQ2NvbIz4+Hnv37kWHDh2wdOlSjTJPnToVGzZsQM+ePTFhwgTVZfD16tXDxYsXy/X1cXBwwIsvvoivv/4aOTk5CAkJUXv9jz/+wHvvvYeBAweiSZMmKCkpwYYNGyCTyfDqq6+qths+fDiOHTtWrmHaquTr6wsA+PjjjxEaGgp9fX0EBwerFXFPvPLKK/jkk08QFhaG9u3b49KlS/j5559VvW7VrWHDhvj0008xffp03L17F/369YO5uTliY2Px22+/4e2338bkyZPL9T345JNP8Oeff6J3796oV68eUlJS8N1338HNzU215lCLFi3wwgsvYPr06cjIyICNjQ02bdpUqrh6lmHDhmHLli145513cOTIEXTo0AEKhQLXr1/Hli1bcPDgwVKTv0m7WABRtSlrsT7g8WJuI0eORHJyMlauXImDBw/C09MTP/30E7Zu3aq2yJiY9PX1sXv3btUcAiMjI/Tv3x/vvfcevL29qyVD06ZNsW3bNvzvf//D5MmT4eTkhDFjxsDe3h6jRo2q0vfy9PTE2bNnMWfOHKxduxbp6elwcHCAj48PZs6c+cx9hw4dilWrVuG7775DZmYmnJycEBISgtmzZ5eaF7V06VL8/PPPmDlzJoqLizF48GAsXrxYbZhgyJAhcHFxweeff44vvvgChYWFcHV1RadOndSutilvZmdnZxw5cgTjxo3D559/DltbW7zzzjtwcXHBG2+8Ue6vUUhICA4fPgxzc3PVkN0T3t7eCAoKwu7du3Hv3j2YmJjA29sb+/fvxwsvvFDu99AWf39/zJ07FytWrMCBAwegVCoRGxtbZgH00UcfIS8vD7/88gs2b96MNm3aYO/evZg2bZoIyR+bNm0amjRpgkWLFmHOnDkAHs+j6dGjh6p4Ls/3oE+fPrh79y5+/PFHpKWlwc7ODl26dMGcOXPUrjT9+eefMXr0aHz++eewsrLCG2+8gRdffBEvvfRSufJKpVLs2LEDixYtwvr16/Hbb7/BxMQEDRo0wIQJE8o1lEdVSyJU958dRER4vLpuWFgYIiMj+ZcvEVU7zgEiIiIincMCiIiIiHQOCyAiIiLSOZwDRERERDqHPUBERESkc1gAERERkc7hOkBlUCqVuH//PszNzblkORERUS0hCAJycnLg4uLy3HsxsgAqw/3793ljOiIioloqISHhuTccZgFUBnNzcwCPv4AWFhYipyEiIqLyyM7OhlwuV32OPwsLoDI8GfaysLBgAURERFTLlGf6CidBExERkc5hAUREREQ6hwUQERER6RwWQERERKRzWAARERGRzmEBRERERDqHBRARERHpHBZAREREpHNYABEREZHO4UrQ1UihFBARm4GUnAI4mBuhrYcNZFLebJWIiKi6sQCqJgcuJ2HO7qtIyipQtTlbGmFWsCd6tnQWMRkREZHu4RBYNThwOQljfopWK34AIDmrAGN+isaBy0kiJSMiItJNLIC0TKEUMGf3VQhlvPakbc7uq1Aoy9qCiIiItIEFkJZFxGaU6vn5NwFAUlYBImIzqi8UERGRjmMBpGUpOU8vfiqyHREREVUeCyAtczA3qtLtiIiIqPJYAGlZWw8bOFsa4WkXu0vw+Gqwth421RmLiIhIp7EA0jKZVIJZwZ4A8NQiaFawJ9cDIiIiqkYsgKpBz5bOWD60DZws1Ye5JAC+DmnNdYCIiIiqGRdCrCY9WzrjJU8nRMRmIDm7AAsPXEdSVgGSsh6JHY2IiEjnsAeoGsmkErRraIv+Pq6YEtQUAPD98VjkF5WInIyIiEi3sAASSR9vF9SzNUFGXhF+Ph0vdhwiIiKdwgJIJHoyKcZ2bQQAWPnnHRQUK0ROREREpDtYAImofxtXuFoZIy23EBsj2AtERERUXVgAiUhfJsW7LzYEAKw4dpu9QERERNWEBZDIXvN1g7OlER5kF2JrVKLYcYiIiHQCCyCRGerJ8E6Xx71Ay4/cQlGJUuREREREdR8LoBogxF8OB3ND3M8qwPZo9gIRERFpGwugGsBIX4a3OzcAACw7egvFCvYCERERaRMLoBri9YB6sDMzQELGI+w8f1/sOERERHUaC6AawthAhjc7/d0LdOQWFEpB5ERERER1FwugGmTYC/VgbaKP2LQ87LnIXiAiIiJtYQFUg5ga6uGNjh4AgCV/3IKSvUBERERawQKohhnevj4sjPRwKyUX+y8nix2HiIioTmIBVMNYGOljlKoX6CZ7gYiIiLSABVANFNbeA2aGerienIPfrz4QOw4REVGdwwKoBrI00cfI9vUBPO4FEgT2AhEREVUlFkA11KiOHjAxkOHK/Wz8cT1F7DhERER1CgugGsrG1ADD2tUDACz+4xZ7gYiIiKoQC6Aa7K1ODWCkL8WFhEz8eTNN7DhERER1BgugGszOzBCvB/zdCxTOuUBERERVhQVQDTe6cwMY6EkRFfcQp26nix2HiIioThC9AFq2bBnq168PIyMjBAQEICIi4pnbZ2ZmYuzYsXB2doahoSGaNGmCffv2VeqYNZmDhREG+8sBAIv/uClyGiIiorpB1AJo8+bNmDRpEmbNmoXo6Gh4e3sjKCgIKSllX/VUVFSEl156CXfv3sW2bdsQExOD1atXw9XVtcLHrA1Gd2kIfZkEp+9kICI2Q+w4REREtZ5EEHFiSUBAAPz9/bF06VIAgFKphFwux7hx4zBt2rRS269YsQJffPEFrl+/Dn19/So5Zlmys7NhaWmJrKwsWFhYVPDsqtZHv13CL2fi0amxHTa8ESB2HCIiohpHk89v0XqAioqKEBUVhcDAwH/CSKUIDAzEqVOnytxn165daNeuHcaOHQtHR0e0bNkS8+bNg0KhqPAxAaCwsBDZ2dlqj5pmTJeG0JNKcPxmGqLjH4odh4iIqFYTrQBKS0uDQqGAo6OjWrujoyOSk8u+CeidO3ewbds2KBQK7Nu3DzNmzMBXX32FTz/9tMLHBID58+fD0tJS9ZDL5ZU8u6ontzHBgDaPh/qWhHMuEBERUWWIPglaE0qlEg4ODli1ahV8fX0REhKCjz/+GCtWrKjUcadPn46srCzVIyEhoYoSV62xLzaCTCrBkZhUXEzMFDsOERFRrSVaAWRnZweZTIYHD9Rv9vngwQM4OTmVuY+zszOaNGkCmUymamvevDmSk5NRVFRUoWMCgKGhISwsLNQeNVE9W1P09XYBACz545bIaYiIiGov0QogAwMD+Pr6Ijw8XNWmVCoRHh6Odu3alblPhw4dcOvWLSiVSlXbjRs34OzsDAMDgwods7YZ260RJBLg0NUHuHq/5s1VIiIiqg1EHQKbNGkSVq9ejXXr1uHatWsYM2YM8vLyEBYWBgAYPnw4pk+frtp+zJgxyMjIwIQJE3Djxg3s3bsX8+bNw9ixY8t9zNquob0ZXmn1uBdo6RHOBSIiIqoIPTHfPCQkBKmpqZg5cyaSk5PRunVrHDhwQDWJOT4+HlLpPzWaXC7HwYMH8f7776NVq1ZwdXXFhAkT8OGHH5b7mHXBuG6NsPvCfey7lIyY5Bw0dTIXOxIREVGtIuo6QDVVTVwH6L/G/BSF/ZeTEeztgiWDfcSOQ0REJLpasQ4QVc573RoBAPZcvI9bKbkipyEiIqpdWADVUi1cLBHY3BGCAHx3hFeEERERaYIFUC02vvvjXqCdF+7jblqeyGmIiIhqDxZAtVgrNyt0bWoPhVLAd0fZC0RERFReLIBquXHdGgMAtkffQ0JGvshpiIiIagcWQLWcbz1rdGpshxKlgOXHbosdh4iIqFZgAVQHPOkF2no2AfczH4mchoiIqOZjAVQHtPWwwQsNbFCsELCSvUBERETPxQKojhj/dy/QxsgEpGQXiJyGiIioZmMBVEe0a2gLv3rWKCpRYuWfd8SOQ0REVKOxAKojJBIJxnV/3Av085k4pOUWipyIiIio5mIBVId0bmwHb7kVCoqVWH2cvUBERERPwwKoDpFIJBj/9z3CNpyKQ0ZekciJiIiIaiYWQHVMt2YOaOFigfwiBX48ESt2HCIiohqJBVAdI5FIVOsCrT15F1n5xSInIiIiqnlYANVBPTwd0czJHLmFJVhzkr1ARERE/8UCqA6SSiV47++5QD+eiEVOAXuBiIiI/k1P7ACkHb1aOqORw03cSsnF2pN34VfPBik5BXAwN0JbDxvIpBKxIxIREYmGBVAdJZNKMK5bI0zYdB5fH7oBQfjnNWdLI8wK9kTPls7iBSQiIhIRh8DqMH3p42/vv4sfAEjOKsCYn6Jx4HKSCKmIiIjExwKojlIoBczde7XM157UQ3N2X4VCKZS5DRERUV3GAqiOiojNQFLW02+KKgBIyipARGxG9YUiIiKqIVgA1VEpOeW7I3x5tyMiIqpLWADVUQ7mRlW6HRERUV3CAqiOauthA2dLIzztYncJHl8N1tbDpjpjERER1QgsgOoomVSCWcGeAFBmESQAmBXsyfWAiIhIJ7EAqsN6tnTG8qFt4GRZepirhYsFglo4iZCKiIhIfFwIsY7r2dIZL3k6ISI2Ayk5BShWKDF9+yVcuZ+NX6Pv4TVfN7EjEhERVTsWQDpAJpWgXUNb1fOUnEIsPBCDT3ZfQafGdnC04ERoIiLSLRwC00Fvd2qAVm6WyC4owce/XYLw36WiiYiI6jgWQDpITybFF695Q18mweFrKdh5/r7YkYiIiKoVCyAd1dTJHOO7NQYAzN59hQsiEhGRTmEBpMPe6doQLVwskJlfjBk7LnMojIiIdAYLIB2m//dQmJ5UgoNXHmDPRd4dnoiIdAMLIB3n6WKBsS82AgDM2nUF6bmFIiciIiLSPhZAhLEvNkIzJ3Nk5BVh5q4rYschIiLSOhZABAM9Kb4c6A2ZVIK9F5Ow/xKHwoiIqG6rUAEUHx+P48eP4+DBg4iOjkZhIYdNaruWrpYY06UhAGDGzsvIyCsSOREREZH2lLsAunv3Lj788EPUq1cPHh4e6NKlC3r16gU/Pz9YWlripZdewtatW6FUKrWZl7RoXPdGaOJohrTcIszZzaEwIiKqu8pVAI0fPx7e3t6IjY3Fp59+iqtXryIrKwtFRUVITk7Gvn370LFjR8ycOROtWrVCZGSktnOTFhjqyfDFa96QSoCd5+/j0NUHYkciIiLSinIVQKamprhz5w62bNmCYcOGoWnTpjA3N4eenh4cHBzQrVs3zJo1C9euXcOXX36JhIQEjUIsW7YM9evXh5GREQICAhAREfHUbdeuXQuJRKL2MDJSv5fVyJEjS23Ts2dPjTLpKm+5Fd7u/Hgo7KPfLiEzn0NhRERU95TrZqjz588v9wE1LTQ2b96MSZMmYcWKFQgICMA333yDoKAgxMTEwMHBocx9LCwsEBMTo3oukUjKzLFmzRrVc0NDQ41y6bKJgY1x6Goybqfm4ZM9V/H1oNZiRyIiIqpSGk+CfvToEfLz81XP4+Li8M033+DgwYMVCvD111/jrbfeQlhYGDw9PbFixQqYmJjgxx9/fOo+EokETk5Oqoejo2OpbQwNDdW2sba2rlA+XWSkL8PC17whkQDbo+/hj+scCiMiorpF4wKob9++WL9+PQAgMzMTAQEB+Oqrr9CvXz8sX75co2MVFRUhKioKgYGB/wSSShEYGIhTp049db/c3FzUq1cPcrkcffv2xZUrpSfsHj16FA4ODmjatCnGjBmD9PT0px6vsLAQ2dnZag9d51vPGm908AAAfLT9MrIeFYuciIiIqOpoXABFR0ejU6dOAIBt27bB0dERcXFxWL9+PRYvXqzRsdLS0qBQKEr14Dg6OiI5ObnMfZo2bYoff/wRO3fuxE8//QSlUon27dsjMTFRtU3Pnj2xfv16hIeHY8GCBTh27Bh69eoFhUJR5jHnz58PS0tL1UMul2t0HnXVBz2awsPOFMnZBfhs71Wx4xAREVUZjQug/Px8mJubAwB+//13DBgwAFKpFC+88ALi4uKqPOB/tWvXDsOHD0fr1q3RpUsXbN++Hfb29li5cqVqm9DQUPTp0wdeXl7o168f9uzZg8jISBw9erTMY06fPh1ZWVmqh6aTuOsqYwMZFr7WChIJsOVsIo7dSBU7EhERUZXQuABq1KgRduzYgYSEBBw8eBA9evQAAKSkpMDCwkKjY9nZ2UEmk+HBA/U5Jg8ePICTk1O5jqGvrw8fHx/cunXrqds0aNAAdnZ2T93G0NAQFhYWag96zL++DUa0qw8AmP7rReQUcCiMiIhqP40LoJkzZ2Ly5MmoX78+AgIC0K5dOwCPe4N8fHw0OpaBgQF8fX0RHh6ualMqlQgPD1cd93kUCgUuXboEZ2fnp26TmJiI9PT0Z25DTze1Z1O425jgflYB5u+/LnYcIiKiStO4AHrttdcQHx+Ps2fP4sCBA6r27t27Y9GiRRoHmDRpElavXo1169bh2rVrGDNmDPLy8hAWFgYAGD58OKZPn67a/pNPPsHvv/+OO3fuIDo6GkOHDkVcXBzefPNNAI8nSE+ZMgWnT5/G3bt3ER4ejr59+6JRo0YICgrSOB8BJgZ6WPBqKwDAL2fi8detNJETERERVU651gECAHd3d/Tp0wd9+vRBt27dSg1RtW3btkIBQkJCkJqaipkzZyI5ORmtW7fGgQMHVBOj4+PjIZX+U6c9fPgQb731FpKTk2FtbQ1fX1+cPHkSnp6eAACZTIaLFy9i3bp1yMzMhIuLC3r06IG5c+dyLaBKaNfQFsNeqIcNp+Pw4a8XcXBiZ5galvvHh4iIqEaRCIIglGfDY8eOYdeuXdi1axdSU1MRFBSEPn36oHfv3rCystJyzOqVnZ0NS0tLZGVlcT7Qv+QVlqDHoj9xL/MRhrerh0/6thQ7EhERkYomn9/lLoD+7cqVK9i1axd27tyJ8+fPo3379qreoQYNGlQ4eE3BAujpTtxMw9AfzgAANr39Al5oYCtyIiIiosc0+fzWeA4QALRo0QLTp0/H6dOnERsbi8GDByM8PBwtW7ZEy5YtsXfv3goFp5qvY2M7DG7rDgCYuu0i8otKRE5ERESkuQr1AD1Nfn4+Dh48CHNzc7XVnWsb9gA9W05BMYIW/Yn7WQUI61Afs4JbiB2JiIhIo8/vCs9iTUlJQUpKCpRKpVp7//79K3pIqiXMjfQx/9VWGPFjBNaevIveXs7wq28jdiwiIqJy07gAioqKwogRI3Dt2jX8t/NIIpE89XYTVLd0aWKPQX5u2HI2EVO2XcT+CZ1gpC8TOxYREVG5aDwHaNSoUWjSpAlOnjyJO3fuIDY2VvW4c+eONjJSDfVxb084WhgiNi0PX/0eI3YcIiKictN4DpC5uTnOnTuHRo0aaSuT6DgHqPz+uP4Ao9aehVQCbBvTHm3crcWOREREOkqrV4F1794dFy5cqHA4qlu6NXPEAB9XKAVgytYLKCjmECgREdV8Gs8B+v777zFixAhcvnwZLVu2hL6+vtrrffr0qbJwVDvMDPbE8VtpuJ2ah2/Db+LDns3EjkRERPRMGhdAp06dwl9//YX9+/eXeo2ToHWTlYkBPuvXEm9viMLKY7fRs4UTvOVWYsciIiJ6Ko2HwMaNG4ehQ4ciKSkJSqVS7cHiR3f1aOGEPt4uj4fCtl1AYQl/FoiIqObSuABKT0/H+++/r7pZKdETs/u0gJ2ZAW48yMXSP26JHYeIiOipNC6ABgwYgCNHjmgjC9VyNqYGmPv3DVK/O3obl+9liZyIiIiobBrPAWrSpAmmT5+OEydOwMvLq9Qk6PHjx1dZOKp9enk5o7eXM/ZeSsLkrRew672OMNCr0C3niIiItEbjdYA8PDyefjCJpE4shsh1gConLbcQPRb9iYy8IkwMbIyJgU3EjkRERDpAq/cCi42NrXAw0g12ZoaY3acFxm88h6V/3EIPTyd4urCQJCKimoNjE6QVwa2cEdTCESVKAVO2XUCxQvn8nYiIiKpJuQqgzz//HI8ePSrXAc+cOYO9e/dWKhTVfhKJBHP7tYSViT6u3M/GymO3xY5ERESkUq4C6OrVq3B3d8e7776L/fv3IzU1VfVaSUkJLl68iO+++w7t27dHSEgIzM3NtRaYag8HcyPMCvYEACwOv4UbD3JETkRERPRYuQqg9evX4/DhwyguLsaQIUPg5OQEAwMDmJubw9DQED4+Pvjxxx8xfPhwXL9+HZ07d9Z2bqol+rV2RfdmDihSKDFl6wWUcCiMiIhqAI2vAlMqlbh48SLi4uLw6NEj2NnZoXXr1rCzs9NWxmrHq8Cq1oPsArz09TFkF5Tgw57NMKZrQ7EjERFRHaTJ57fGBZAuYAFU9baeTcCUbRdhoCfFvvGd0MjBTOxIRERUx2jy+c2rwKhavObrhq5N7VFUosSUbRegULLuJiIi8bAAomohkUgwr78XzA31cC4+E2v+4npSREQkHhZAVG1crIzxce/mAIAvDsbgTmquyImIiEhXsQCiahXiL0enxnYoLFHiw18vQsmhMCIiEgELIKpWEokE8wd4wdRAhsi7D7Hu1F2xIxERkQ4q173ABgwYUO4Dbt++vcJhSDe4WZtg+svN8b8dl7HwQAy6NXNAPVtTsWMREZEOKVcPkKWlpephYWGB8PBwnD17VvV6VFQUwsPDYWlpqbWgVLcMaeuOdg1s8ahYganbOBRGRETVS+N1gD788ENkZGRgxYoVkMlkAACFQoF3330XFhYW+OKLL7QStDpxHaDqEZ+ej6Bv/sSjYgXm9m2BYe3qix2JiIhqMa0uhGhvb48TJ06gadOmau0xMTFo37490tPTNU9cw7AAqj5r/4rF7N1XYWIgw8GJnSG3MRE7EhER1VJaXQixpKQE169fL9V+/fp1KJW8zxNpZni7+mhb3wb5RQpM234RXJiciIiqQ7kmQf9bWFgY3njjDdy+fRtt27YFAJw5cwaff/45wsLCqjwg1W1SqQQLX2uFnt/+ib9upWNjRAKGBLiLHYuIiOo4jQugL7/8Ek5OTvjqq6+QlJQEAHB2dsaUKVPwwQcfVHlAqvvq25lico+m+HTvNczbdw1dmtrD1cpY7FhERFSHVepmqNnZ2QBQ5+bJcA5Q9VMoBQxccRLR8Zno3MQe68L8IZFIxI5FRES1iNZvhlpSUoLDhw9j48aNqg+p+/fvIzeXtzagipFJJfhioDcM9KT480Yqtp5NFDsSERHVYRoXQHFxcfDy8kLfvn0xduxYpKamAgAWLFiAyZMnV3lA0h0N7c3wwUtNAABz915FclaByImIiKiu0rgAmjBhAvz8/PDw4UMYG/8zT6N///4IDw+v0nCke97s1ADecivkFJTgo98u8aowIiLSCo0LoOPHj+N///sfDAwM1Nrr16+Pe/fuVVkw0k0yqQRfvtYKBjIp/riegt/O8WeKiIiqnsYFkFKphEKhKNWemJgIc3PzKglFuq2xozkmBDYGAMzedQUp2RwKIyKiqqVxAdSjRw988803qucSiQS5ubmYNWsWXn755QqFWLZsGerXrw8jIyMEBAQgIiLiqduuXbsWEolE7WFkZKS2jSAImDlzJpydnWFsbIzAwEDcvHmzQtlIHKM7N4CXqyWyC0rw8Y7LHAojIqIqpXEB9NVXX+Gvv/6Cp6cnCgoKMGTIENXw14IFCzQOsHnzZkyaNAmzZs1CdHQ0vL29ERQUhJSUlKfuY2FhgaSkJNUjLi5O7fWFCxdi8eLFWLFiBc6cOQNTU1MEBQWhoIA9CbWFnkyKLwa2gr5MgkNXH2DXhftiRyIiojqkQusAlZSUYNOmTbh48SJyc3PRpk0bvP7662qTossrICAA/v7+WLp0KYDHQ2xyuRzjxo3DtGnTSm2/du1aTJw4EZmZmWUeTxAEuLi44IMPPlBdlZaVlQVHR0esXbsWoaGhz83EdYBqjsXhN/H1oRuwNtHH7+93gb25odiRiIiohtLk81vjlaABQE9PD0OHDq1QuH8rKipCVFQUpk+frmqTSqUIDAzEqVOnnrpfbm4u6tWrB6VSiTZt2mDevHlo0aIFACA2NhbJyckIDAxUbW9paYmAgACcOnWqzAKosLAQhYWFqudPFngk8Y3p2hAHLifjalI2Zu68jOVDfcWOREREdUCFFkLcsGEDOnbsCBcXF9Xw06JFi7Bz506NjpOWlgaFQgFHR0e1dkdHRyQnJ5e5T9OmTfHjjz9i586d+Omnn6BUKtG+fXskJj5eOO/Jfpocc/78+bC0tFQ95HK5RudB2qP/91CYnlSC/ZeTsfdiktiRiIioDtC4AFq+fDkmTZqEXr164eHDh6orwqytrdUmR2tLu3btMHz4cLRu3RpdunTB9u3bYW9vj5UrV1b4mNOnT0dWVpbqkZCQUIWJqbJauFji3a4NAQAzd15Gem7hc/YgIiJ6No0LoCVLlmD16tX4+OOPoaf3zwian58fLl26pNGx7OzsIJPJ8ODBA7X2Bw8ewMnJqVzH0NfXh4+PD27dugUAqv00OaahoSEsLCzUHlSzvNetMZo5mSM9rwizd18VOw4REdVyGhdAsbGx8PHxKdVuaGiIvLw8jY5lYGAAX19ftRWklUolwsPD0a5du3IdQ6FQ4NKlS3B2dgYAeHh4wMnJSe2Y2dnZOHPmTLmPSTWPgZ4UX7zmDZlUgt0X7uPA5bKHM4mIiMpD4wLIw8MD58+fL9V+4MABNG/eXOMAkyZNwurVq7Fu3Tpcu3YNY8aMQV5eHsLCwgAAw4cPV5sk/cknn+D333/HnTt3EB0djaFDhyIuLg5vvvkmgMfrEk2cOBGffvopdu3ahUuXLmH48OFwcXFBv379NM5HNYeXmyVGd24AAPjfjst4mFckciIiIqqtNL4KbNKkSRg7diwKCgogCAIiIiKwceNGzJ8/H99//73GAUJCQpCamoqZM2ciOTkZrVu3xoEDB1STmOPj4yGV/lOnPXz4EG+99RaSk5NhbW0NX19fnDx5Ep6enqptpk6diry8PLz99tvIzMxEx44dceDAgVILJlLtM757Y/x+9QFupeTikz1XsSiktdiRiIioFqrQOkA///wzZs+ejdu3bwMAXFxcMGfOHLzxxhtVHlAMXAeoZjsX/xCvLj8JpQB8P9wPgZ6Oz9+JiIjqPE0+vytUAD2Rn5+P3NxcODg4VPQQNRILoJpv/r5rWPnnHTiYG+LQ+11gaaIvdiQiIhKZJp/fFVoHCABSUlIQFRWFmJgYpKamVvQwRBXy/ktN0MDOFCk5hZi7l1eFERGRZjQugHJycjBs2DC4uLigS5cu6NKlC1xcXDB06FBkZWVpIyNRKUb6MnwxsBUkEmBbVCKOxDz93nFERET/pXEB9Oabb+LMmTPYu3cvMjMzkZmZiT179uDs2bMYPXq0NjISlcm3ng1GdfAAAEz/9RKyC4pFTkRERLWFxnOATE1NcfDgQXTs2FGt/fjx4+jZs6fGawHVRJwDVHs8KlKg17d/4m56PkL95fj81VZiRyIiIpFodQ6Qra0tLC0tS7VbWlrC2tpa08MRVYqxgQwL/i56NkUm4PhNzkcjIqLn07gA+t///odJkyap3Vg0OTkZU6ZMwYwZM6o0HFF5BDSwxYh29QAA0369hKxHxTh1Ox07z9/DqdvpUCgrfKEjERHVURoPgT2571ZhYSHc3d0BPF6s0NDQEI0bN1bbNjo6uuqSViMOgdU+eYUl6Pntn0jIeAQTAxnyixSq15wtjTAr2BM9WzqLmJCIiLRNk89vjVeC5u0kqCYyNdTDq23c8M3hm2rFDwAkZxVgzE/RWD60DYsgIiICUIECaNasWdrIQVQpCqWAzZEJZb4mAJAAmLP7Kl7ydIJMKqnWbEREVPNoPAcoISEBiYmJqucRERGYOHEiVq1aVaXBiDQREZuBpKyCp74uAEjKKkBEbEb1hSIiohpL4wJoyJAhOHLkCIDHk58DAwMRERGBjz/+GJ988kmVByQqj5Scpxc/FdmOiIjqNo0LoMuXL6Nt27YAgC1btsDLywsnT57Ezz//jLVr11Z1PqJycTA3qtLtiIiobtO4ACouLoahoSEA4PDhw+jTpw8AoFmzZkhKSqradETl1NbDBs6WRnjW7B5jfRm8XEuvYUVERLpH4wKoRYsWWLFiBY4fP45Dhw6hZ8+eAID79+/D1ta2ygMSlYdMKsGsYE8AeGoR9KhYgf7f/YUbD3KqLxgREdVIGhdACxYswMqVK9G1a1cMHjwY3t7eAIBdu3aphsaIxNCzpTOWD20DJ0v1YS5nSyO8H9gEDuaGuJmSiz5LT2DL2QRouAQWERHVIRovhAgACoUC2dnZare+uHv3LkxMTODg4FClAcXAhRBrN4VSQERsBlJyCuBgboS2HjaQSSVIyy3E+5vP4/jNNABAfx9XfNqvJUwNNV4NgoiIaiBNPr8rVADVdSyA6i6lUsCKP2/jq99vQKEU0MDOFEuHtIGnC7/PRES1nVZvhkpUm0mlErzbtRE2vf0CnC2NcCctD/2++ws/n4njkBgRkQ5hAUQ6yb++DfaN74RuzRxQVKLEx79dxnsbzyGnoFjsaEREVA1YAJHOsjY1wPfD/fDxy82hJ5Vg78UkvLLkBC4lZokdjYiItKxSBVBBAVfVpdpNKpXgrc4NsOWddnC1MkZcej5eXX4Sa/+K5ZAYEVEdpnEBpFQqMXfuXLi6usLMzAx37twBAMyYMQM//PBDlQckqg5t3K2xb3wn9PB0RJFCidm7r+Kdn6KQlc8hMSKiukjjAujTTz/F2rVrsXDhQhgYGKjaW7Zsie+//75KwxFVJ0sTfawc5ovZwZ4wkElx8MoD9F5yHOfiH4odjYiIqpjGBdD69euxatUqvP7665DJZKp2b29vXL9+vUrDEVU3iUSCkR088OuY9nC3MUHiw0cYuOIUVv95h0NiRER1iMYF0L1799CoUaNS7UqlEsXFHC6gusHLzRJ7xndE71bOKFEK+GzfNby57iwe5hWJHY2IiKqAxgWQp6cnjh8/Xqp927Zt8PHxqZJQRDWBhZE+lg72waf9WsJAT4rw6yl4efFxnL2bIXY0IiKqJI3vATBz5kyMGDEC9+7dg1KpxPbt2xETE4P169djz5492shIJBqJRIKhL9RDG3drvPdLNO6k5SFk1Wl80KMJ3uncEFLps+4/T0RENZXGPUB9+/bF7t27cfjwYZiammLmzJm4du0adu/ejZdeekkbGYlE5+ligV3jOqJfaxcolAIWHojByLWRSMstFDsaERFVAO8FVgbeC4yeRhAEbD2biJm7LqOgWAkHc0MsHuyDFxrYih2NiEjnVdu9wHJzc5Gdna32IKrLJBIJBvnLseu9jmjsYIaUnEIMWX0a3x6+CYWSf0sQEdUWGhdAsbGx6N27N0xNTWFpaQlra2tYW1vDysoK1tbW2shIVOM0cTTHzvc6YKCvG5QCsOjwDQz74QxScrg6OhFRbaDxEFiHDh0gCAImTJgAR0dHSCTqk0C7dOlSpQHFwCEw0sT26ET8b8dl5BcpYGdmgG9CfNCxsZ3YsYiIdI4mn98aF0BmZmaIiopC06ZNKxWyJmMBRJq6lZKL936JxvXkHEgkwHsvNsKE7o2hJ+P9homIqotW5wD5+/sjISGhwuGI6qJGDmbYMbYDhgS4QxCAJX/cwpDvzyA5i0NiREQ1kcY9QLdv38Y777yDoUOHomXLltDX11d7vVWrVlUaUAzsAaLK2HXhPj7afgm5hSWwMTXAV4O88WJTB7FjERHVeZp8fmu8EGJqaipu376NsLAwVZtEIoEgCJBIJFAoFJonJqpD+ni7oJWrJd7bGI3L97IRtiYSo7s0wOQeTaHPITEiohpB4x4gT09PNG/eHFOnTi1zEnS9evWqNKAY2ANEVaGwRIF5e69h3ak4AEAbdyssGdIGrlbGIicjIqqbtDoJ2tTUFBcuXCjzhqh1BQsgqkr7LyVh6q8XkVNQAktjfXw50BsveTqKHYuIqM7R6iTobt264cKFCxUOV5Zly5ahfv36MDIyQkBAACIiIsq136ZNmyCRSNCvXz+19pEjR0Iikag9evbsWaWZicqrl5cz9o3vBG83S2Q9KsZb689i7p6rKCpRih2NiEhnaTwHKDg4GO+//z4uXboELy+vUpOg+/Tpo9HxNm/ejEmTJmHFihUICAjAN998g6CgIMTExMDB4ekTR+/evYvJkyejU6dOZb7es2dPrFmzRvXc0NBQo1xEVUluY4Kt77THwgPX8f2JWPxwIhZn72Zg6ZA2kNuYiB2PiEjnaDwEJpU+vdOoIpOgAwIC4O/vj6VLlwIAlEol5HI5xo0bh2nTppW5j0KhQOfOnTFq1CgcP34cmZmZ2LFjh+r1kSNHlmrTBIfASJsOX32AD7ZeQNajYpgb6WHhq63Qy8tZ7FhERLWeVofAlErlUx+aFj9FRUWIiopCYGDgP4GkUgQGBuLUqVNP3e+TTz6Bg4MD3njjjaduc/ToUTg4OKBp06YYM2YM0tPTNcpGpC2Bno7YN6ETfOtZI6egBGN+jsbMnZdRUMwrKImIqouo1+SmpaVBoVDA0VF9QqijoyOSk5PL3OfEiRP44YcfsHr16qcet2fPnli/fj3Cw8OxYMECHDt2DL169XpqgVZYWMibulK1crUyxqa3X8A7XRoCANafisOry08iNi1P5GRERLpB4zlAAJCXl4djx44hPj4eRUVFaq+NHz++SoKVJScnB8OGDcPq1athZ/f0ey2Fhoaq/u3l5YVWrVqhYcOGOHr0KLp3715q+/nz52POnDlayUz0NPoyKab1aoYXGthg0pYLuHI/G68sPo55A7zQt7Wr2PGIiOo0jecAnTt3Di+//DLy8/ORl5cHGxsbpKWlwcTEBA4ODrhz5065j1VUVAQTExNs27ZN7UquESNGIDMzEzt37lTb/vz58/Dx8YFMJlO1KZWPr6SRSqWIiYlBw4YNy3wve3t7fPrppxg9enSp1woLC1FYWKh6np2dDblczjlAVG2SswowftM5RMRmAAAGt5VjVnALGOnLnrMnERE9odU5QO+//z6Cg4Px8OFDGBsb4/Tp04iLi4Ovry++/PJLjY5lYGAAX19fhIeHq9qUSiXCw8PRrl27Uts3a9YMly5dwvnz51WPPn364MUXX8T58+chl8vLfJ/ExESkp6fD2bnsiaaGhoawsLBQexBVJydLI/zyZgDGd2sEiQTYGJGAfsv+wq2UXLGjERHVSRoXQOfPn8cHH3wAqVQKmUyGwsJCyOVyLFy4EB999JHGASZNmoTVq1dj3bp1uHbtGsaMGYO8vDzVrTaGDx+O6dOnAwCMjIzQsmVLtYeVlRXMzc3RsmVLGBgYIDc3F1OmTMHp06dx9+5dhIeHo2/fvmjUqBGCgoI0zkdUXfRkUkzq0RQbRgXAzswQ15NzELzkBH6NShQ7GhFRnaNxAaSvr6+6FN7BwQHx8fEAAEtLywrdJT4kJARffvklZs6cidatW+P8+fM4cOCAamJ0fHw8kpKSyn08mUyGixcvok+fPmjSpAneeOMN+Pr64vjx41wLiGqFjo3tsG9CR3RoZItHxQp8sPUCJm+9gPyiErGjERHVGRrPAerRowdGjhyJIUOG4K233sLFixcxfvx4bNiwAQ8fPsSZM2e0lbXacB0gqgkUSgHLjtzCN4dvQCkAjRzMsGxIGzR1Mhc7GhFRjaTVOUDz5s1TzaX57LPPYG1tjTFjxiA1NRWrVq2qWGIiKkUmlWB898b45a0X4GhhiFspueiz9AQ2RcRDw79biIjoPzTuAdIF7AGimiY9txCTtlzAsRupAIC+rV3wWX8vmBlWaCULIqI6Sas9QERU/WzNDLFmpD8+7NkMMqkEO8/fR58lJ3DlfpbY0YiIaqVy9QD5+PhAIpGU64DR0dGVDiU29gBRTRYVl4Fxv5zD/awCGOhJMeMVTwwNcC/3/1EiorpKk8/vcvWf/3uRQiISl289G+wd3wlTtl3A4WspmLHjMk7fTsf8V71gYaQvdjwiolqhXAWQtbU13n77bRgZGSE+Ph5ubm7PvCs8EWmXtakBVg/3ww8nYvH5/uvYeykJl+5lYekQH7RysxI7HhFRjVeuITA9PT3cv38fDg4OkMlkSEpKgoODQ3XkEwWHwKg2OZ+Qifd+iUbiw0fQl0kwvVdzhHWozyExItI5VT4J2sXFBb/++ivi4uIgCAISExMRHx9f5oOIqldruRX2ju+Eni2cUKwQ8Mmeqxi9IQpZ+cViRyMiqrHK1QO0atUqjBs3DiUlT1+JVhAESCQSKBSKKg0oBvYAUW0kCALWn4rDZ3uvoUihhKuVMZYM8UEbd2uxoxERVQtNPr/LvQ5QTk4O4uLi0KpVKxw+fBi2trZlbuft7a154hqGBRDVZpfvZWHsL9GIS8+HnlSCqT2b4s2ODSCVckiMiOo2rRRAT6xbtw6hoaF1+r5aLICotsspKMb07Zew5+Lj++h1a+aALwd6w8bUQORkRETao9UCSBewAKK6QBAEbIxIwOzdV1BUooSThREWD/ZBWw8bsaMREWkFV4ImIkgkEgwJcMfOsR3QwN4UydkFGLz6NJYduQWlkn/3EJFuYwFEVMc1d7bA7vc6YoCPKxRKAV8cjMGINRFIzSkUOxoRkWhYABHpAFNDPXw1yBsLX2sFI30pjt9Mw8uLj+Pk7TSxoxERiUKjAqi4uBgNGzbEtWvXtJWHiLREIpFgkJ8cu9/riCaOZkjNKcTQ78/gm8M3oOCQGBHpGI0KIH19fRQUFGgrCxFVg8aO5tg5tiMG+blBKQDfHL6Jod+fQUo2/28Tke7QeAhs7NixWLBgwTMXRSSims3YQIaFr3ljUYg3TAxkOHUnHb2+PY4/b6SKHY2IqFpofBl8//79ER4eDjMzM3h5ecHU1FTt9e3bt1dpQDHwMnjSJbdTczH252hcT86BRAK827Uh3g9sAj0ZpwgSUe2iyed3ue4G/29WVlZ49dVXKxyOiGqWhvZm2DG2A+buuYqfz8Rj2ZHbiIjNwOLBPnC2NBY7HhGRVnAhxDKwB4h01Z6L9zHt10vILSyBtYk+vh7UGi82cxA7FhFRuXAhRCKqkFdauWDv+I7wcrXEw/xihK2NxPx911CsUIodjYioSlWoB2jbtm3YsmUL4uPjUVRUpPZadHR0lYUTC3uASNcVligwf991rD15FwDg426FJYN94GZtIm4wIqJn0GoP0OLFixEWFgZHR0ecO3cObdu2ha2tLe7cuYNevXpVODQR1RyGejLM7tMCK4b6wsJID+fiM/Hyt8dx8Eqy2NGIiKqExgXQd999h1WrVmHJkiUwMDDA1KlTcejQIYwfPx5ZWVnayEhEIunZ0gl7x3eCt9wK2QUlGL0hCnN2X0FhiULsaERElaJxARQfH4/27dsDAIyNjZGTkwMAGDZsGDZu3Fi16YhIdHIbE2wd3Q5vdfIAAKz56y5eW34Kcel5IicjIqo4jQsgJycnZGRkAADc3d1x+vRpAEBsbCx4QRlR3WSgJ8XHvT3xwwg/WJno49K9LLyy+AT2XkwSOxoRUYVoXAB169YNu3btAgCEhYXh/fffx0svvYSQkBD079+/ygMSUc3Rvbkj9o3vBL961sgpLMHYX6Lxvx2XUFDMITEiql00vgpMqVRCqVRCT+/xGoqbNm3CyZMn0bhxY4wePRoGBgZaCVqdeBUY0bOVKJT4+tANfHf0NgCgubMFlg3xQQN7M5GTEZEu0+TzmwshloEFEFH5HLuRikmbzyM9rwimBjLMG+CFvq1dxY5FRDpK6wVQZmYmIiIikJKSAqVSfYG04cOHa3q4GocFEFH5PcguwIRN53D6zuO5gSF+cszu0wLGBjKRkxGRrtFqAbR79268/vrryM3NhYWFBSQSyT8Hk0hUE6RrMxZARJpRKAV8G34TS/64CUEAmjiaYdmQNmjsaC52NCLSIVotgJo0aYKXX34Z8+bNg4lJ3VwVlgUQUcWcvJWGCZvPIzWnEMb6MnzStwUG+snFjkVEOkKrK0Hfu3cP48ePr7PFDxFVXPtGdtg3vhM6NrLDo2IFpmy7iElbziOvsETsaEREajQugIKCgnD27FltZCGiOsDe3BDrRrXF5B5NIJUA26Pvoc/SE7ienC12NCIiFT1Nd+jduzemTJmCq1evwsvLC/r6+mqv9+nTp8rCEVHtJJNK8F63xvCvb4Pxm87hdmoe+i79C7P7tECov1xt7iARkRg0ngMklT6900gikUChqP0LonEOEFHVSc8txAdbL+BoTCoAINjbBfP6t4S5kf5z9iQi0oxW5wA9WQixrEddKH6IqGrZmhnixxH+mN6rGWRSCXZfuI/gJSdw+R5vnkxE4tG4ACIi0pRUKsHoLg2xZXQ7uFoZ4256PgZ8dxLrT93lPQSJSBTlGgJbvHgx3n77bRgZGWHx4sXP3Hb8+PFVFk4sHAIj0p7M/CJM3noRh689AAD0aumEz19tBUtjDokRUeVU+TpAHh4eOHv2LGxtbeHh4fH0g0kkuHPnjsaBly1bhi+++ALJycnw9vbGkiVL0LZt2+fut2nTJgwePBh9+/bFjh07VO2CIGDWrFlYvXo1MjMz0aFDByxfvhyNGzcuVx4WQETaJQgCfvzrLj7ffw3FCgFyG2MsHdwG3nIrsaMRUS1Wq+4FtnnzZgwfPhwrVqxAQEAAvvnmG2zduhUxMTFwcHB46n53795Fx44d0aBBA9jY2KgVQAsWLMD8+fOxbt06eHh4YMaMGbh06RKuXr0KIyOj52ZiAURUPS4kZOK9jdFIyHgEfZkE03o1x6gO9XmVGBFVSK0qgAICAuDv74+lS5cCeDzJWi6XY9y4cZg2bVqZ+ygUCnTu3BmjRo3C8ePHkZmZqSqABEGAi4sLPvjgA0yePBkAkJWVBUdHR6xduxahoaHPzcQCiKj6ZD0qxrRfL2L/5WQAQGBzR3w5sBWsTAxETkZEtY0mn98arwMkCAK2bduGI0eOlHkz1O3bt5f7WEVFRYiKisL06dNVbVKpFIGBgTh16tRT9/vkk0/g4OCAN954A8ePH1d7LTY2FsnJyQgMDFS1WVpaIiAgAKdOnSpXAURE1cfSWB/fvd4GP52Ow9w913D42gO8/O1xLBniA996NmLHI6I6SuOrwCZOnIhhw4YhNjYWZmZmsLS0VHtoIi0tDQqFAo6Ojmrtjo6OSE5OLnOfEydO4IcffsDq1avLfP3Jfpocs7CwENnZ2WoPIqo+EokEw9rVx/Z326O+rQnuZxVg0MrTWHHsNpRKXiVGRFVP4x6gDRs2YPv27Xj55Ze1keeZcnJyMGzYMKxevRp2dnZVdtz58+djzpw5VXY8IqqYlq6W2DO+Ez7afgm7LtzH5/uv4/SddHw10Bu2ZoZixyOiOkTjHiBLS0s0aNCgSt7czs4OMpkMDx48UGt/8OABnJycSm1/+/Zt3L17F8HBwdDT04Oenh7Wr1+PXbt2QU9PD7dv31btV95jAsD06dORlZWleiQkJFTJ+RGR5swM9fBtaGvMH+AFQz0pjsak4uXFx3HmTjoAQKEUcOp2Onaev4dTt9OhYA8REVWAxj1As2fPxpw5c/Djjz/C2Ni4Um9uYGAAX19fhIeHo1+/fgAeT4IODw/He++9V2r7Zs2a4dKlS2pt//vf/5CTk4Nvv/0Wcrkc+vr6cHJyQnh4OFq3bg3g8aSoM2fOYMyYMWXmMDQ0hKEh/7okqikkEgkGt3WHj7sVxv4cjdupeRi8+jRe8XJGxN2HSM4uUG3rbGmEWcGe6NnSWcTERFTbaFwADRo0CBs3boSDgwPq169f6mao0dHRGh1v0qRJGDFiBPz8/NC2bVt88803yMvLQ1hYGABg+PDhcHV1xfz582FkZISWLVuq7W9lZQUAau0TJ07Ep59+isaNG6sug3dxcVEVWURUOzRzssCu9zpixs7L2B59D7suJpXaJjmrAGN+isbyoW1YBBFRuWlcAI0YMQJRUVEYOnQoHB0dK71eR0hICFJTUzFz5kwkJyejdevWOHDggGoSc3x8/DNvwFqWqVOnIi8vD2+//TYyMzPRsWNHHDhwoFxrABFRzWJqqIcvXvNG+LUUZD0qLvW6AEACYM7uq3jJ0wkyKdcQIqLn03gdIFNTUxw8eBAdO3bUVibRcR0goprl1O10DF59+rnbbXzrBbRraFsNiYioJtLq3eDlcjmLAiKqVik5Bc/fCMDRmBROiiaictG4APrqq68wdepU3L17VwtxiIhKczAv3/D1yj/voOOCP/D1oRtIfJiv5VREVJtpPARmbW2N/Px8lJSUwMTEpNQk6IyMjCoNKAYOgRHVLAqlgI4L/kByVgGe9gvL1EAGPZkEWY9KAAASCdCpsT1C/eUIbO4IAz2N/94jolpGq7fC+Oabbyqai4ioQmRSCWYFe2LMT9GQAGpF0JMpz18N8kbXpg74/eoDbIqIx8nb6fjzRir+vJEKW1MDvOrrhkF+cjRyMBPhDIiophH9Zqg1EXuAiGqmA5eTMGf3VSRlPX8doLj0PGw5m4CtZxORklOoavevb41Qf3e87OUMYwNZtWUnIu2rtrvBFxQUoKioSK2tLhQMLICIai6FUkBEbAZScgrgYG6Eth42z7z0vUShxJGYVGyOjMcf11PwZI60uaEe+vq4INTfHS1dNbuPIRHVTFotgPLy8vDhhx9iy5YtSE9PL/W6QqHQLG0NxAKIqG5KzirAtqgEbD6bgISMR6r2lq4WCPF3R9/WLrAw0n/GEYioJtNqATR27FgcOXIEc+fOxbBhw7Bs2TLcu3cPK1euxOeff47XX3+9UuFrAhZARHWbUing5O10bIqMx+9XHqBIoQQAGOlL0dvLBaFt5fCrZ13phV6JqHpptQByd3fH+vXr0bVrV1hYWCA6OhqNGjXChg0bsHHjRuzbt69S4WsCFkBEuiMjrwjboxOxOTIBN1NyVe0N7U0R6u+OAW1ceSd6olpCqwWQmZkZrl69Cnd3d7i5uWH79u1o27YtYmNj4eXlhdzc3OcfpIZjAUSkewRBQHR8JjZFxGPPxSQ8Kn48nK8vk6CHpxNC/OXo2MgOUt5qg6jG0upl8A0aNEBsbCzc3d3RrFkzbNmyBW3btsXu3btVNyYlIqptJBIJfOtZw7eeNWYGe2L3hSRsiozHxcQs7L2UhL2XkuBqZYwQfzkG+rnB2dJY7MhEVAka9wAtWrQIMpkM48ePx+HDhxEcHAxBEFBcXIyvv/4aEyZM0FbWasMeICJ64sr9LGyJTMBv5+4hu+DxIotSCdCliT1C/N3RvbkD9GVcZJGoJqi2y+ABIC4uDlFRUWjUqBFatWpVmUPVGCyAiOi/CooV2H85CZsiEnAm9p8V7+3MDPGarxtC/OXwsDMVMSERVWsB9ERiYiI++eQTrFq1qioOJyoWQET0LHdSc7H5bAJ+jUpEWu4/a6G90MAGof7u6NnSCUb6XGSRqLqJUgBduHABbdq04TpARKQzihVKhF97gE2RCTh2IxVPfptaGuujv48rQvzlaO7M3yFE1YUFUCWxACIiTd3LfIRtZxOx5WwC7mX+s8iit9wKof5yBHu7wMxQ4+tOiEgDLIAqiQUQEVWUQingxK00bP57kcWSv++9YWIgwyutnBHa1h0+cisuskikBVq9DJ6IiJ5OJpWgSxN7dGlij7TcQmyPTsSmyATcSc3DlrOJ2HI2EU0czRDi744BPq6wNjUQOzKRTip3D9CAAQOe+XpmZiaOHTvGHiAiov8QBAGRdx9iU2Q89l5MQmHJ41tvGMikCGrphFB/Odo1sOUii0SVpJUhsLCwsHK9+Zo1a8q1XU3GAoiItCXrUTF2nb+HjREJuJqUrWp3tzFBiL8cr/m6wdHCSMSERLWXKHOA6hIWQERUHS7fy8LGiHjsOn8fOYWPF1mUSSV4sakDQv3l6NrUHnpcZJGo3FgAVRILICKqTvlFJdh3KRmbIuJxNu6hqt3RwhADfeUY5CeHu62JiAmJagcWQJXEAoiIxHIrJQebIxPwa/Q9ZOT9s8hih0a2CPV3R48WjjDU4yKLRGVhAVRJLICISGyFJQocvpqCTZHxOH4zTdVubaKP/j5uCG0rRxNHcxETEtU8LIAqiQUQEdUkCRn52Ho2AVvOJiI5u0DV3sbdCqH+7ujdyhmmXGSRiAVQZbEAIqKaSKEUcOxGCjZFJCD8egoUfy+yaGaoh2BvF4T6y9HKzZKLLJLOYgFUSSyAiKimS8kpwLaoRGyOTEBcer6qvbmzBUL95ejX2hWWJvoiJiSqfiyAKokFEBHVFkqlgDOxGdgUGY/9l5NR9Pcii4Z6Urzs5YwQfzkCPGzYK0Q6gQVQJbEAIqLaKDO/CDvO3cOmyARcT85RtXvYmSLEX45X27jB3txQxIRE2sUCqJJYABFRbSYIAi4kZmFz5ONFFvOKHt+iSE8qQffmDgj1d0fnJvaQ8dYbVMewAKokFkBEVFfkFZZgz8X72BSZgHPxmap2Z0sjDPSTY5CfG9ysucgi1Q0sgCqJBRAR1UXXk7OxOTIBv527h8z8YgCARAJ0amyPUH85Aps7wkCPt96g2osFUCWxACKiuqygWIHfrz7Apoh4nLydrmq3NTXAq75uGOQnRyMHMxETElUMC6BKYgFERLoiLj0PW84mYOvZRKTkFKra/etbI9TfHS97OcPYgLfeoNqBBVAlsQAiIl1TolDiSEwqNkfG44/rKfh7jUWYG+qhr48LQv3d0dLVUtyQRM/BAqiSWAARkS5LzirAtqgEbD6bgISMR6r2lq4WCPF3R9/WLrAw4iKLVPOwAKokFkBERI8XWTx5Ox2bIuPx+5UHKFI8XmTRSF+K3l4uCG0rh189ay6ySDUGC6BKYgFERKQuI68I26Mf33rjZkquqr2hvSlC/d0xoI0rbM24yCKJiwVQJbEAIiIqmyAIiI7PxKaIeOy5mIRHxY8XWdSXSdDD0wkh/nJ0bGQHKRdZJBGwAKokFkBERM+XU1CM3ReSsCkyHhcTs1TtrlbGCPGXY6CfG5wtjUVMSLpGk8/vGrHi1bJly1C/fn0YGRkhICAAERERT912+/bt8PPzg5WVFUxNTdG6dWts2LBBbZuRI0dCIpGoPXr27Knt0yAi0inmRvoYEuCOXe91xN7xHTGiXT1YGOnhXuYjfH3oBjp8/gfC1kTg4JVkFP89f4iophC9B2jz5s0YPnw4VqxYgYCAAHzzzTfYunUrYmJi4ODgUGr7o0eP4uHDh2jWrBkMDAywZ88efPDBB9i7dy+CgoIAPC6AHjx4gDVr1qj2MzQ0hLW1dbkysQeIiKhiCooV2H85CZsiEnAmNkPVbmdmiNd83RDiL4eHnamICakuq1VDYAEBAfD398fSpUsBAEqlEnK5HOPGjcO0adPKdYw2bdqgd+/emDt3LoDHBVBmZiZ27NhRoUwsgIiIKu9Oai42n03Ar1GJSMstUrW/0MAGof7u6NnSCUb6XGSRqk6tGQIrKipCVFQUAgMDVW1SqRSBgYE4derUc/cXBAHh4eGIiYlB586d1V47evQoHBwc0LRpU4wZMwbp6elPOQoREWlDA3szTO/VHKemd8eKoW3Qtak9JBLg9J0MTNx8HgHzwjF71xVcS8oWOyrpID0x3zwtLQ0KhQKOjo5q7Y6Ojrh+/fpT98vKyoKrqysKCwshk8nw3Xff4aWXXlK93rNnTwwYMAAeHh64ffs2PvroI/Tq1QunTp2CTFb6r43CwkIUFv6zBHx2Nv8zEhFVFX2ZFD1bOqNnS2fcy3yErX/feuNe5iOsPXkXa0/ehbfcCqH+cgR7u8DMUNSPJtIRtfKnzNzcHOfPn0dubi7Cw8MxadIkNGjQAF27dgUAhIaGqrb18vJCq1at0LBhQxw9ehTdu3cvdbz58+djzpw51RWfiEhnuVoZY2JgE4zr1hgnbqVh89+LLF5IyMSFhEzM3XMVwa1cENJWDh+5FRdZJK0RdQ5QUVERTExMsG3bNvTr10/VPmLECGRmZmLnzp3lOs6bb76JhIQEHDx48Knb2Nvb49NPP8Xo0aNLvVZWD5BcLuccICKiapCWW4jt0YnYFJmAO6l5qvYmjmYI8XfHAB9XWJsaiJiQaotaMwfIwMAAvr6+CA8PV7UplUqEh4ejXbt25T6OUqlUK2D+KzExEenp6XB2di7zdUNDQ1hYWKg9iIioetiZGeLtzg0RPqkLtoxuhwFtXGGoJ8WNB7mYu+cqAuaFY9zGc/jrVhqUSi5dR1VD9CGwSZMmYcSIEfDz80Pbtm3xzTffIC8vD2FhYQCA4cOHw9XVFfPnzwfweLjKz88PDRs2RGFhIfbt24cNGzZg+fLlAIDc3FzMmTMHr776KpycnHD79m1MnToVjRo1Ul0mT0RENY9EIkFbDxu09bDBrOAW2HX+HjZGJOBqUjZ2X7iP3Rfuw93GBCH+crzm6wZHCyOxI1MtJnoBFBISgtTUVMycORPJyclo3bo1Dhw4oJoYHR8fD6n0n46qvLw8vPvuu0hMTISxsTGaNWuGn376CSEhIQAAmUyGixcvYt26dcjMzISLiwt69OiBuXPnwtCQ96khIqoNLI31MaxdfQxrVx+XErOwKTIeO8/fR3xGPr44GIOvD93Ai00dEOovR9em9tCT1Yh1fakWEX0doJqI6wAREdU8+UUl2HsxCZsjE3A27qGq3dHCEAN95RjkJ4e7rYmICUlstWohxJqIBRARUc12KyUHmyMT8Gv0PWTk/bPIYodGtgj1d0ePFo4w1OMii7qGBVAlsQAiIqodCksUOHw1BZsi43H8Zpqq3dpEH/193BDaVo4mjuYiJqTqxAKoklgAERHVPgkZ+dh6NgFbziYiObtA1d7G3Qqh/u7o3coZplxksU5jAVRJLICIiGqvEoUSf95MxaaIBIRfT4Hi70vnzQz1EOztglB/OVq5WXKRxTqIBVAlsQAiIqobUnIKsC0qEZsjExCXnq9qb+5sgVB/Ofq1doWlib6ICakqsQCqJBZARER1i1Ip4ExsBjZFxmP/5WQUlSgBAIZ6Urzs5YwQfzkCPGzYK1TLsQCqJBZARER1V2Z+EXacu4dNkQm4npyjavewM0WIvxyvtnGDvTnXjauNWABVEgsgIqK6TxAEXEjMwubIeOw6fx95RQoAgJ5Ugu7NHRDa1h2dG9tDJmWvUG3BAqiSWAAREemWvMIS7Ll4H5siE3AuPlPV7mxphIF+cgzyc4ObNRdZrOlYAFUSCyAiIt11PTkbmyMT8Nu5e8jMLwYASCRAp8b2CPWXI7C5Iwz0eOuNmogFUCWxACIiooJiBQ5eScbmyAScvJ2uarc1NcCrvm4Y5CdHIwczERPSf7EAqiQWQERE9G9x6XnYHJmArVGJSM0pVLW3rW+DEH85XvZyhrEBb70hNhZAlcQCiIiIylKiUOJITCo2R8bjj+sp+HuNRZgb6qGvjwtC/d3R0tVS3JA6jAVQJbEAIiKi50nOKsC2qARsPpuAhIxHqvaWrhYI8XdH39YusDDiIovViQVQJbEAIiKi8lIqBZy8nY5NkfH4/coDFCkeL7JopC9Fby8XDG4rh289ay6yWA1YAFUSCyAiIqqIjLwibI9OxKbIBNxKyVW1N7Q3Rai/Owa0cYWtGRdZ1BYWQJXEAoiIiCpDEARExz/EpogE7LmYhEfFjxdZ1JdJ0MPTCSH+cnRsZAcpF1msUiyAKokFEBERVZWcgmLsvpCETZHxuJiYpWp3tTJGiL8cA/3c4GxpLGLCuoMFUCWxACIiIm24cj8LW/5eZDG7oAQAIJUAXZrYI7StO7o1c4C+jIssVhQLoEpiAURERNpUUKzA/stJ2BiRgIjYDFW7nZkhXvN1Q6i/HPXtTEVMWDuxAKokFkBERFRd7qTmYvPZBPwalYi03CJV+wsNbBDq746eLZ1gpM9FFsuDBVAlsQAiIqLqVqxQIvzaA2yKTMCxG6l48ulsaayP/j6uCPGXo7kzP5OehQVQJbEAIiIiMd3LfIStZxOw9Wwi7mX+s8iit9wKof5yBHu7wMxQT8SENRMLoEpiAURERDWBQingxK00bIqIx6GrD1Dy9703TAxkCG7lgpC2cvjIrbjI4t9YAFUSCyAiIqpp0nIL8WtUIjZHJuBOWp6qvamjOUL85ejv4wprUwMRE4qPBVAlsQAiIqKaShAERN59iE2R8dh7MQmFJY9vvWEgkyKopRNC/eVo18BWJxdZZAFUSSyAiIioNsh6VIxd5+9hY0QCriZlq9rdbUwQ4i/Ha75ucLQwEjFh9WIBVEksgIiIqLa5lJiFTZHx2Hn+PnILHy+yKJNK8GJTB4T6y9G1qT306vgiiyyAKokFEBER1Vb5RSXYezEJmyMTcDbuoard0cIQA33lGOQnh7utiYgJtYcFUCWxACIiorrgVkoONkUkYPu5e8jI+2eRxQ6NbBHq744eLRxhqFd3FllkAVRJLICIiKguKSxR4PDVFGyKjMfxm2mqdmsTffT3cUNoWzmaOJqLmLBqsACqJBZARERUVyVk5GPr2QRsOZuI5OwCVXsbdyuE+rvjFW9nmBjUzkUWWQBVEgsgIiKq60oUSvx5MxWbIhIQfj0Fir8XWTQz1EOwtwtC/eVo5WZZqxZZZAFUSSyAiIhIl6RkF2Bb9ONFFuPS81XtzZ0tEOovR7/WrrA00RcxYfmwAKokFkBERKSLlEoBZ2IzsCkyHvsvJ6Po70UWDfWkeNnLGSH+cgR42NTYXiEWQJXEAoiIiHRdZn4Rdpy7h02RCbienKNq97AzRYi/HK+2cYO9uaGICUtjAVRJLICIiIgeEwQBFxKzsCkiHrsu3Ed+kQIAoCeVoHtzB4S2dUfnxvaQ1YBbb7AAqiQWQERERKXlFpZg78X72BiRgPMJmap2Z0sjDPSTY5CfG9ysxVtkkQVQJbEAIiIierbrydnYHJmA387dQ2Z+MQBAIgE6NbZHqL8cgc0dYaCnfusNhVJARGwGUnIK4GBuhLYeNlXac8QCqJJYABEREZVPQbECB68kY3NkAk7eTle125oa4FVfNwzyk6ORgxkOXE7CnN1XkZT1z9pDzpZGmBXsiZ4tnaskiyaf3zXirmjLli1D/fr1YWRkhICAAERERDx12+3bt8PPzw9WVlYwNTVF69atsWHDBrVtBEHAzJkz4ezsDGNjYwQGBuLmzZvaPg0iIiKdY6QvQ9/WrvjlrRdwbEpXvNu1IezNDZGeV4RVf95B4NfHEPjVMbzzU7Ra8QMAyVkFGPNTNA5cTqr23KIXQJs3b8akSZMwa9YsREdHw9vbG0FBQUhJSSlzexsbG3z88cc4deoULl68iLCwMISFheHgwYOqbRYuXIjFixdjxYoVOHPmDExNTREUFISCgoIyj0lERESVV8/WFFN7NsOpad2wergfujdzgATArdTcMrd/MgQ1Z/dV1UKM1UX0IbCAgAD4+/tj6dKlAAClUgm5XI5x48Zh2rRp5TpGmzZt0Lt3b8ydOxeCIMDFxQUffPABJk+eDADIysqCo6Mj1q5di9DQ0Ocej0NgREREVWPvxSSM/SX6udttfOsFtGtoW6n3qjVDYEVFRYiKikJgYKCqTSqVIjAwEKdOnXru/oIgIDw8HDExMejcuTMAIDY2FsnJyWrHtLS0REBAQLmOSURERFWnRKks13YpOdU7SiPq3c7S0tKgUCjg6Oio1u7o6Ijr168/db+srCy4urqisLAQMpkM3333HV566SUAQHJysuoY/z3mk9f+q7CwEIWFharn2dnZFTofIiIiUudgblSl21UV0ecAVYS5uTnOnz+PyMhIfPbZZ5g0aRKOHj1a4ePNnz8flpaWqodcLq+6sERERDqsrYcNnC2N8LSL3SV4fDVYWw+b6owlbgFkZ2cHmUyGBw8eqLU/ePAATk5OT91PKpWiUaNGaN26NT744AO89tprmD9/PgCo9tPkmNOnT0dWVpbqkZCQUJnTIiIior/JpBLMCvYEgFJF0JPns4I9q30laVELIAMDA/j6+iI8PFzVplQqER4ejnbt2pX7OEqlUjWE5eHhAScnJ7VjZmdn48yZM089pqGhISwsLNQeREREVDV6tnTG8qFt4GSpPszlZGmE5UPbVNk6QJoQdQ4QAEyaNAkjRoyAn58f2rZti2+++QZ5eXkICwsDAAwfPhyurq6qHp758+fDz88PDRs2RGFhIfbt24cNGzZg+fLlAACJRIKJEyfi008/RePGjeHh4YEZM2bAxcUF/fr1E+s0iYiIdFrPls54ydNJqytBa0L0AigkJASpqamYOXMmkpOT0bp1axw4cEA1iTk+Ph5S6T8dVXl5eXj33XeRmJgIY2NjNGvWDD/99BNCQkJU20ydOhV5eXl4++23kZmZiY4dO+LAgQMwMqreCVZERET0D5lUUulL3auK6OsA1URcB4iIiKj2qTXrABERERGJgQUQERER6RwWQERERKRzWAARERGRzmEBRERERDqHBRARERHpHBZAREREpHNYABEREZHOEX0l6JroydqQ2dnZIichIiKi8nryuV2eNZ5ZAJUhJycHACCXy0VOQkRERJrKycmBpaXlM7fhrTDKoFQqcf/+fZibm0MiEecmbVUtOzsbcrkcCQkJOnF7D55v3cbzrdt4vnWbNs9XEATk5OTAxcVF7T6iZWEPUBmkUinc3NzEjqEVFhYWOvEf7Ameb93G863beL51m7bO93k9P09wEjQRERHpHBZAREREpHNYAOkIQ0NDzJo1C4aGhmJHqRY837qN51u38XzrtppyvpwETURERDqHPUBERESkc1gAERERkc5hAUREREQ6hwUQERER6RwWQHXY/Pnz4e/vD3Nzczg4OKBfv36IiYkRO1a1+fzzzyGRSDBx4kSxo2jNvXv3MHToUNja2sLY2BheXl44e/as2LG0RqFQYMaMGfDw8ICxsTEaNmyIuXPnluu+P7XBn3/+ieDgYLi4uEAikWDHjh1qrwuCgJkzZ8LZ2RnGxsYIDAzEzZs3xQlbBZ51vsXFxfjwww/h5eUFU1NTuLi4YPjw4bh//754gSvped/ff3vnnXcgkUjwzTffVFu+qlae87127Rr69OkDS0tLmJqawt/fH/Hx8dWSjwVQHXbs2DGMHTsWp0+fxqFDh1BcXIwePXogLy9P7GhaFxkZiZUrV6JVq1ZiR9Gahw8fokOHDtDX18f+/ftx9epVfPXVV7C2thY7mtYsWLAAy5cvx9KlS3Ht2jUsWLAACxcuxJIlS8SOViXy8vLg7e2NZcuWlfn6woULsXjxYqxYsQJnzpyBqakpgoKCUFBQUM1Jq8azzjc/Px/R0dGYMWMGoqOjsX37dsTExKBPnz4iJK0az/v+PvHbb7/h9OnTcHFxqaZk2vG88719+zY6duyIZs2a4ejRo7h48SJmzJgBIyOj6gkokM5ISUkRAAjHjh0TO4pW5eTkCI0bNxYOHTokdOnSRZgwYYLYkbTiww8/FDp27Ch2jGrVu3dvYdSoUWptAwYMEF5//XWREmkPAOG3335TPVcqlYKTk5PwxRdfqNoyMzMFQ0NDYePGjSIkrFr/Pd+yRERECACEuLi46gmlRU8738TERMHV1VW4fPmyUK9ePWHRokXVnk0byjrfkJAQYejQoeIEEgSBPUA6JCsrCwBgY2MjchLtGjt2LHr37o3AwECxo2jVrl274Ofnh4EDB8LBwQE+Pj5YvXq12LG0qn379ggPD8eNGzcAABcuXMCJEyfQq1cvkZNpX2xsLJKTk9V+ri0tLREQEIBTp06JmKz6ZGVlQSKRwMrKSuwoWqFUKjFs2DBMmTIFLVq0EDuOVimVSuzduxdNmjRBUFAQHBwcEBAQ8MxhwarGAkhHKJVKTJw4ER06dEDLli3FjqM1mzZtQnR0NObPny92FK27c+cOli9fjsaNG+PgwYMYM2YMxo8fj3Xr1okdTWumTZuG0NBQNGvWDPr6+vDx8cHEiRPx+uuvix1N65KTkwEAjo6Oau2Ojo6q1+qygoICfPjhhxg8eHCdvWHoggULoKenh/Hjx4sdRetSUlKQm5uLzz//HD179sTvv/+O/v37Y8CAATh27Fi1ZODd4HXE2LFjcfnyZZw4cULsKFqTkJCACRMm4NChQ9U3hiwipVIJPz8/zJs3DwDg4+ODy5cvY8WKFRgxYoTI6bRjy5Yt+Pnnn/HLL7+gRYsWOH/+PCZOnAgXF5c6e870eEL0oEGDIAgCli9fLnYcrYiKisK3336L6OhoSCQSseNonVKpBAD07dsX77//PgCgdevWOHnyJFasWIEuXbpoPQN7gHTAe++9hz179uDIkSNwc3MTO47WREVFISUlBW3atIGenh709PRw7NgxLF68GHp6elAoFGJHrFLOzs7w9PRUa2vevHm1XUEhhilTpqh6gby8vDBs2DC8//77OtHj5+TkBAB48OCBWvuDBw9Ur9VFT4qfuLg4HDp0qM72/hw/fhwpKSlwd3dX/f6Ki4vDBx98gPr164sdr8rZ2dlBT09P1N9h7AGqwwRBwLhx4/Dbb7/h6NGj8PDwEDuSVnXv3h2XLl1SawsLC0OzZs3w4YcfQiaTiZRMOzp06FBqWYMbN26gXr16IiXSvvz8fEil6n+3yWQy1V+TdZmHhwecnJwQHh6O1q1bAwCys7Nx5swZjBkzRtxwWvKk+Ll58yaOHDkCW1tbsSNpzbBhw0rNWwwKCsKwYcMQFhYmUirtMTAwgL+/v6i/w1gA1WFjx47FL7/8gp07d8Lc3Fw1T8DS0hLGxsYip6t65ubmpeY3mZqawtbWtk7Oe3r//ffRvn17zJs3D4MGDUJERARWrVqFVatWiR1Na4KDg/HZZ5/B3d0dLVq0wLlz5/D1119j1KhRYkerErm5ubh165bqeWxsLM6fPw8bGxu4u7tj4sSJ+PTTT9G4cWN4eHhgxowZcHFxQb9+/cQLXQnPOl9nZ2e89tpriI6Oxp49e6BQKFS/w2xsbGBgYCBW7Ap73vf3vwWevr4+nJyc0LRp0+qOWiWed75TpkxBSEgIOnfujBdffBEHDhzA7t27cfTo0eoJKNr1Z6R1AMp8rFmzRuxo1aYuXwYvCIKwe/duoWXLloKhoaHQrFkzYdWqVWJH0qrs7GxhwoQJgru7u2BkZCQ0aNBA+Pjjj4XCwkKxo1WJI0eOlPl/dsSIEYIgPL4UfsaMGYKjo6NgaGgodO/eXYiJiRE3dCU863xjY2Of+jvsyJEjYkevkOd9f/+rtl8GX57z/eGHH4RGjRoJRkZGgre3t7Bjx45qyycRhDqyhCoRERFROXESNBEREekcFkBERESkc1gAERERkc5hAUREREQ6hwUQERER6RwWQERERKRzWAARERGRzmEBRERERDqHBRARqUgkEuzYseOZ24wcOVKjWy/cvXsXEokE58+fr1S2um7YsGGYN2+eKO999epVuLm5IS8vT5T3JxIDCyCiOkrTQgUAkpKS0KtXLwBPL1y+/fZbrF27tmpC/q1r166QSCSQSCQwMjKCp6cnvvvuuyp9j5rswoUL2LdvH8aPH1/ufY4ePQqJRILMzMxKv7+npydeeOEFfP3115U+FlFtwQKIiFScnJxgaGj4zG0sLS1hZWVV5e/91ltvISkpCVevXsWgQYMwduxYbNy4scxti4qKqvz9K6symZYsWYKBAwfCzMysChNpJiwsDMuXL0dJSYloGYiqEwsgIh3RtWtXjB8/HlOnToWNjQ2cnJwwe/ZstW3+PQTm4eEBAPDx8YFEIkHXrl0BlO5ZOnDgADp27AgrKyvY2trilVdewe3btzXOZ2JiAicnJzRo0ACzZ89G48aNsWvXLlX29957DxMnToSdnR2CgoIAAJcvX0avXr1gZmYGR0dHDBs2DGlpaapjbtu2DV5eXjA2NoatrS0CAwNVwzxHjx5F27ZtYWpqCisrK3To0AFxcXFlniMATJw4UfU1qEym/1IoFNi2bRuCg4PV2jds2AA/Pz+Ym5vDyckJQ4YMQUpKCoDHvXMvvvgiAMDa2hoSiQQjR45U5Ro3bhwmTpwIa2trODo6YvXq1cjLy0NYWBjMzc3RqFEj7N+/X+39XnrpJWRkZODYsWPP/V4R1QUsgIh0yLp162BqaoozZ85g4cKF+OSTT3Do0KEyt42IiAAAHD58GElJSdi+fXuZ2+Xl5WHSpEk4e/YswsPDIZVK0b9/fyiVykplNTY2VutVWbduHQwMDPDXX39hxYoVyMzMRLdu3eDj44OzZ8/iwIEDePDgAQYNGgTg8XDe4MGDMWrUKFy7dg1Hjx7FgAEDIAgCSkpK0K9fP3Tp0gUXL17EqVOn8Pbbb0MikWiUUdNMZbl48SKysrLg5+en1l5cXIy5c+fiwoUL2LFjB+7evasqcuRyOX799VcAQExMDJKSkvDtt9+q5bKzs0NERATGjRuHMWPGYODAgWjfvj2io6PRo0cPDBs2DPn5+ap9DAwM0Lp1axw/flyjrwFRrVVt950nomo1YsQIoW/fvqrnXbp0ETp27Ki2jb+/v/Dhhx+qngMQfvvtN0EQBCE2NlYAIJw7d+6Zx/2v1NRUAYBw6dKlZx7n37p06SJMmDBBEARBKCkpETZs2CAAEJYuXap63cfHR22fuXPnCj169FBrS0hIEAAIMTExQlRUlABAuHv3bqn3S09PFwAIR48eLTNPWec4YcIEoUuXLmqZNc1Ult9++02QyWSCUqks8/UnIiMjBQBCTk6OIAiCcOTIEQGA8PDhQ7Xt/vt9LikpEUxNTYVhw4ap2pKSkgQAwqlTp9T27d+/vzBy5Mhn5iCqK9gDRKRDWrVqpfbc2dlZNaxSUTdv3sTgwYPRoEEDWFhYoH79+gCA+Ph4jY7z3XffwczMDMbGxnjrrbfw/vvvY8yYMarXfX191ba/cOECjhw5AjMzM9WjWbNmAIDbt2/D29sb3bt3h5eXFwYOHIjVq1fj4cOHAAAbGxuMHDkSQUFBCA4OxrfffoukpCSNz13TTGV59OgRDA0NS/U+RUVFITg4GO7u7jA3N0eXLl0AlO/r+u/vs0wmg62tLby8vFRtjo6OAFDqe29sbKzWK0RUl7EAItIh+vr6as8lEkmlh6qCg4ORkZGB1atX48yZMzhz5gwAzScFv/766zh//jxiY2ORl5eHr7/+GlLpP7+iTE1N1bbPzc1FcHAwzp8/r/a4efMmOnfuDJlMhkOHDmH//v3w9PTEkiVL0LRpU8TGxgIA1qxZg1OnTqF9+/bYvHkzmjRpgtOnTwMApFIpBEFQe7/i4uJSmTXNVBY7Ozvk5+erfb3y8vIQFBQECwsL/Pzzz4iMjMRvv/0GoHxf17K+z/9ue1Js/fd7n5GRAXt7++cen6gu0BM7ABHVTAYGBgAeT9J9mvT0dMTExGD16tXo1KkTAODEiRMVej9LS0s0atSo3Nu3adMGv/76K+rXrw89vbJ/lUkkEnTo0AEdOnTAzJkzUa9ePfz222+YNGkSgMcTvH18fDB9+nS0a9cOv/zyC1544QXY29vj8uXLasc6f/58qcKiIpn+q3Xr1gAer8Xz5N/Xr19Heno6Pv/8c8jlcgDA2bNn1fYrz/dHU5cvX8Zrr71WZccjqsnYA0REZXJwcICxsbFqIm9WVlapbaytrWFra4tVq1bh1q1b+OOPP1TFhbaNHTsWGRkZGDx4MCIjI3H79m0cPHgQYWFhUCgUOHPmDObNm4ezZ88iPj4e27dvR2pqKpo3b47Y2FhMnz4dp06dQlxcHH7//XfcvHkTzZs3BwB069YNZ8+exfr163Hz5k3MmjWrVEFUkUxlsbe3R5s2bdQKR3d3dxgYGGDJkiW4c+cOdu3ahblz56rtV69ePUgkEuzZswepqanIzc2txFfz8ZVl9+7dQ2BgYKWOQ1RbsAAiojLp6elh8eLFWLlyJVxcXNC3b99S20ilUmzatAlRUVFo2bIl3n//fXzxxRfVks/FxQV//fUXFAoFevToAS8vL0ycOBFWVlaQSqWwsLDAn3/+iZdffhlNmjTB//73P3z11Vfo1asXTExMcP36dbz66qto0qQJ3n77bYwdOxajR48GAAQFBWHGjBmYOnUq/P39kZOTg+HDh1c609O8+eab+Pnnn1XP7e3tsXbtWmzduhWenp74/PPP8eWXX6rt4+rqijlz5mDatGlwdHTEe++9V8Gv5GMbN25Ejx49UK9evUodh6i2kAj/HegmIqJq9ejRIzRt2hSbN29Gu3btqv39i4qK0LhxY/zyyy/o0KFDtb8/kRjYA0REJDJjY2OsX7/+mQsmalN8fDw++ugjFj+kU9gDRERERDqHPUBERESkc1gAERERkc5hAUREREQ6hwUQERER6RwWQERERKRzWAARERGRzmEBRERERDqHBRARERHpHBZAREREpHP+Dy0RM43bbEuSAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(P_initial, velocity_flame_Q10,\"-o\")\n",
    "plt.xlabel(\"Initial Pressure (atm)\")\n",
    "plt.ylabel(\"Laminar flame speed (m/s)\");\n",
    "plt.title('Laminar flame speed vs. Initial Pressure')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5c385473",
   "metadata": {},
   "outputs": [],
   "source": [
    "flame_thickness_values = []\n",
    "'''\n",
    "#print(len(density_flame_values[0]))\n",
    "for n in range(len(velocity_flame_Q10)):\n",
    "    flame_thickness = []\n",
    "    for m in range(0,len(density_flame_values[0])):\n",
    "        laminar_thickness = thermal_conductivity_values[n][m]/\\\n",
    "        (density_flame_values[n][m]*velocity_values_Q10[n][m]*specific_heat_values[n][m])\n",
    "        flame_thickness.append(laminar_thickness)\n",
    "    flame_thickness_values.append(flame_thickness)\n",
    "'''\n",
    "for n in range(len(velocity_flame_Q10)):\n",
    "    laminar_thickness = thermal_conductivity_values[n][0]/\\\n",
    "        (density_flame_values[n][0]*velocity_values_Q10[n][0]*specific_heat_values[n][0])\n",
    "    flame_thickness_values.append(laminar_thickness*1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "bbe87d17",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.1391107421412993, 0.07812907656219849, 0.046378313023988996, 0.028375506861429044, 0.018584812635420327]\n"
     ]
    }
   ],
   "source": [
    "flame_thickness_values_test = []\n",
    "for n in range(len(velocity_flame_Q10)):\n",
    "    laminar_thickness_test = np.mean(thermal_conductivity_values[n])/\\\n",
    "        (np.mean(density_flame_values[n])*np.mean(velocity_values_Q10[n])*np.mean(specific_heat_values[n]))\n",
    "    flame_thickness_values_test.append(laminar_thickness_test*1000)\n",
    "print(flame_thickness_values_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4901a166",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(P_initial,flame_thickness_values_test,\"-o\")\n",
    "plt.xlabel(\"Initial Pressure (atm)\")\n",
    "plt.ylabel(\"Flame thickness (mm)\");\n",
    "plt.title('Flame thickness (mm) vs. Initial Pressure')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a783f9ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.603304852577955\n"
     ]
    }
   ],
   "source": [
    "print(density_flame_values[0][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dbedcd1e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
